Paper Digest: NAACL 2021 Highlights
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TABLE 1: Paper Digest: NAACL 2021 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Knowledge Router: Learning Disentangled Representations for Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, this paper proposes to learn disentangled representations of KG entities – a new method that disentangles the inner latent properties of KG entities. |
Shuai Zhang; Xi Rao; Yi Tay; Ce Zhang; |
| 2 | Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. |
Fenia Christopoulou; Makoto Miwa; Sophia Ananiadou; |
| 3 | Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel deep learning model to simultaneously solve the four tasks of IE in a single model (called FourIE). |
Minh Van Nguyen; Viet Lai; Thien Huu Nguyen; |
| 4 | Abstract Meaning Representation Guided Graph Encoding and Decoding for Joint Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To take advantage of such similarity, we propose a novel AMR-guided framework for joint information extraction to discover entities, relations, and events with the help of a pre-trained AMR parser. |
Zixuan Zhang; Heng Ji; |
| 5 | A Frustratingly Easy Approach for Entity and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a simple pipelined approach for entity and relation extraction, and establish the new state-of-the-art on standard benchmarks (ACE04, ACE05 and SciERC), obtaining a 1.7%-2.8% absolute improvement in relation F1 over previous joint models with the same pre-trained encoders. |
Zexuan Zhong; Danqi Chen; |
| 6 | Event Time Extraction and Propagation Via Graph Attention Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper first formulates this problem based on a 4-tuple temporal representation used in entity slot filling, which allows us to represent fuzzy time spans more conveniently. We then propose a graph attention network-based approach to propagate temporal information over document-level event graphs constructed by shared entity arguments and temporal relations. |
Haoyang Wen; Yanru Qu; Heng Ji; Qiang Ning; Jiawei Han; Avi Sil; Hanghang Tong; Dan Roth; |
| 7 | Probing Word Translations in The Transformer and Trading Decoder for Encoder Layers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that this is not quite the case: translation already happens progressively in encoder layers and even in the input embeddings. |
Hongfei Xu; Josef van Genabith; Qiuhui Liu; Deyi Xiong; |
| 8 | Mediators in Determining What Processing BERT Performs First Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As a test-case mediating factor, we consider the prediction’s context length, namely the length of the span whose processing is minimally required to perform the prediction. |
Aviv Slobodkin; Leshem Choshen; Omri Abend; |
| 9 | Automatic Generation of Contrast Sets from Scene Graphs: Probing The Compositional Consistency of GQA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: While most contrast sets were created manually, requiring intensive annotation effort, we present a novel method which leverages rich semantic input representation to automatically generate contrast sets for the visual question answering task. |
Yonatan Bitton; Gabriel Stanovsky; Roy Schwartz; Michael Elhadad; |
| 10 | Multilingual Language Models Predict Human Reading Behavior Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We analyze if large language models are able to predict patterns of human reading behavior. |
Nora Hollenstein; Federico Pirovano; Ce Zhang; Lena J�ger; Lisa Beinborn; |
| 11 | Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, drawing a generalisation about a model’s linguistic knowledge about a specific phenomena based on what a probe is able to learn may be problematic: in this work, we show that semantic cues in training data means that syntactic probes do not properly isolate syntax. |
Rowan Hall Maudslay; Ryan Cotterell; |
| 12 | A Non-Linear Structural Probe Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We examine the case of a structural probe (Hewitt and Manning, 2019), which aims to investigate the encoding of syntactic structure in contextual representations through learning only linear transformations. |
Jennifer C. White; Tiago Pimentel; Naomi Saphra; Ryan Cotterell; |
| 13 | Concealed Data Poisoning Attacks on NLP Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop a new data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input. |
Eric Wallace; Tony Zhao; Shi Feng; Sameer Singh; |
| 14 | Backtranslation Feedback Improves User Confidence in MT, Not Quality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Translating text into a language unknown to the text’s author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. We demonstrate this by showing three ways in which user confidence in the outbound translation, as well as its overall final quality, can be affected: backward translation, quality estimation (with alignment) and source paraphrasing. |
Vil�m Zouhar; Michal Nov�k; Mat�� �ilinec; Ondrej Bojar; Mateo Obreg�n; Robin L. Hill; Fr�d�ric Blain; Marina Fomicheva; Lucia Specia; Lisa Yankovskaya; |
| 15 | Data Filtering Using Cross-Lingual Word Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: First, we analyze the performance of language identification, a tool commonly used for data filtering in the MT community and identify specific weaknesses. Based on our findings, we then propose several novel methods for data filtering, based on cross-lingual word embeddings. |
Christian Herold; Jan Rosendahl; Joris Vanvinckenroye; Hermann Ney; |
| 16 | Improving The Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we enhance the bilingual masked language model pretraining with lexical-level information by using type-level cross-lingual subword embeddings. |
Alexandra Chronopoulou; Dario Stojanovski; Alexander Fraser; |
| 17 | Neural Machine Translation Without Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Surprisingly, replacing the ubiquitous embedding layer with one-hot representations of each byte does not hurt performance; experiments on byte-to-byte machine translation from English to 10 different languages show a consistent improvement in BLEU, rivaling character-level and even standard subword-level models. |
Uri Shaham; Omer Levy; |
| 18 | Counterfactual Data Augmentation for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a data augmentation method for neural machine translation. |
Qi Liu; Matt Kusner; Phil Blunsom; |
| 19 | Cultural and Geographical Influences on Image Translatability of Words Across Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better understand when images are useful for translation, we study image translatability of words, which we define as the translatability of words via images, by measuring intra- and inter-cluster similarities of image representations of words that are translations of each other. |
Nikzad Khani; Isidora Tourni; Mohammad Sadegh Rasooli; Chris Callison-Burch; Derry Tanti Wijaya; |
| 20 | Multilingual BERT Post-Pretraining Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved cross-lingual transferability of the pretrained language models. |
Lin Pan; Chung-Wei Hang; Haode Qi; Abhishek Shah; Saloni Potdar; Mo Yu; |
| 21 | A Million Tweets Are Worth A Few Points: Tuning Transformers for Customer Service Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address this gap by collecting a multilingual social media corpus containing customer service conversations (865k tweets), comparing various pipelines of pretraining and finetuning approaches, applying them on 5 different end tasks. |
Amir Hadifar; Sofie Labat; Veronique Hoste; Chris Develder; Thomas Demeester; |
| 22 | Paragraph-level Rationale Extraction Through Regularization: A Case Study on European Court of Human Rights Cases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we introduce a new application on legal text where, contrary to mainstream literature targeting word-level rationales, we conceive rationales as selected paragraphs in multi-paragraph structured court cases. |
Ilias Chalkidis; Manos Fergadiotis; Dimitrios Tsarapatsanis; Nikolaos Aletras; Ion Androutsopoulos; Prodromos Malakasiotis; |
| 23 | Answering Product-Questions By Utilizing Questions from Other Contextually Similar Products Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel and complementary approach for predicting the answer for such questions, based on the answers for similar questions asked on similar products. |
Ohad Rozen; David Carmel; Avihai Mejer; Vitaly Mirkis; Yftah Ziser; |
| 24 | EnSidNet: Enhanced Hybrid Siamese-Deep Network for Grouping Clinical Trials Into Drug-development Pathways Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we present an approach for the unmet need of drug-development pathway reconstruction, based on an Enhanced hybrid Siamese-Deep Neural Network (EnSidNet). |
Lucia Pagani; |
| 25 | DATE: Detecting Anomalies in Text Via Self-Supervision of Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We use this approach for AD in text, by introducing a novel pretext task on text sequences. |
Andrei Manolache; Florin Brad; Elena Burceanu; |
| 26 | A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a simple, yet effective method, based on identifier anonymization, to handle out-of-vocabulary (OOV) identifiers. |
Nadezhda Chirkova; Sergey Troshin; |
| 27 | Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for clas- sification and sequence labelling) to jointly extract dialogue states. |
Dingmin Wang; Chenghua Lin; Qi Liu; Kam-Fai Wong; |
| 28 | Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a simple yet efficient data augmentation strategy called Augmented SBERT, where we use the cross-encoder to label a larger set of input pairs to augment the training data for the bi-encoder. |
Nandan Thakur; Nils Reimers; Johannes Daxenberger; Iryna Gurevych; |
| 29 | SmBoP: Semi-autoregressive Bottom-up Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decoding step t the top-K sub-trees of height = t. |
Ohad Rubin; Jonathan Berant; |
| 30 | SGL: Speaking The Graph Languages of Semantic Parsing Via Multilingual Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, instead, we reframe semantic parsing towards multiple formalisms as Multilingual Neural Machine Translation (MNMT), and propose SGL, a many-to-many seq2seq architecture trained with an MNMT objective. |
Luigi Procopio; Rocco Tripodi; Roberto Navigli; |
| 31 | Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address this issue and present a unified model to perform cross-lingual SRL over heterogeneous linguistic resources. |
Simone Conia; Andrea Bacciu; Roberto Navigli; |
| 32 | Fool Me Twice: Entailment from Wikipedia Gamification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We release FoolMeTwice (FM2 for short), a large dataset of challenging entailment pairs collected through a fun multi-player game. |
Julian Eisenschlos; Bhuwan Dhingra; Jannis Bulian; Benjamin B�rschinger; Jordan Boyd-Graber; |
| 33 | Meta-Learning for Domain Generalization in Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we use a meta-learning framework which targets zero-shot domain generalization for semantic parsing. |
Bailin Wang; Mirella Lapata; Ivan Titov; |
| 34 | Aspect-Controlled Neural Argument Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present the Arg-CTRL – a language model for argument generation that can be controlled to generate sentence-level arguments for a given topic, stance, and aspect. |
Benjamin Schiller; Johannes Daxenberger; Iryna Gurevych; |
| 35 | Text Generation from Discourse Representation Structures Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs). |
Jiangming Liu; Shay B. Cohen; Mirella Lapata; |
| 36 | APo-VAE: Text Generation in Hyperbolic Space Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate text generation in a hyperbolic latent space to learn continuous hierarchical representations. |
Shuyang Dai; Zhe Gan; Yu Cheng; Chenyang Tao; Lawrence Carin; Jingjing Liu; |
| 37 | DART: Open-Domain Structured Data Record to Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). |
Linyong Nan; Dragomir Radev; Rui Zhang; Amrit Rau; Abhinand Sivaprasad; Chiachun Hsieh; Xiangru Tang; Aadit Vyas; Neha Verma; Pranav Krishna; Yangxiaokang Liu; Nadia Irwanto; Jessica Pan; Faiaz Rahman; Ahmad Zaidi; Mutethia Mutuma; Yasin Tarabar; Ankit Gupta; Tao Yu; Yi Chern Tan; Xi Victoria Lin; Caiming Xiong; Richard Socher; Nazneen Fatema Rajani; |
| 38 | When Being Unseen from MBERT Is Just The Beginning: Handling New Languages With Multilingual Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. |
Benjamin Muller; Antonios Anastasopoulos; Beno�t Sagot; Djam� Seddah; |
| 39 | Multi-Adversarial Learning for Cross-Lingual Word Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We assume instead that, especially across distant languages, the mapping is only piece-wise linear, and propose a multi-adversarial learning method. |
Haozhou Wang; James Henderson; Paola Merlo; |
| 40 | Multi-view Subword Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, standard heuristic algorithms often lead to sub-optimal segmentation, especially for languages with limited amounts of data. In this paper, we take two major steps towards alleviating this problem. |
Xinyi Wang; Sebastian Ruder; Graham Neubig; |
| 41 | MT5: A Massively Multilingual Pre-trained Text-to-Text Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. |
Linting Xue; Noah Constant; Adam Roberts; Mihir Kale; Rami Al-Rfou; Aditya Siddhant; Aditya Barua; Colin Raffel; |
| 42 | MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one and brings their representation spaces closer for effective transfer. |
Mengzhou Xia; Guoqing Zheng; Subhabrata Mukherjee; Milad Shokouhi; Graham Neubig; Ahmed Hassan Awadallah; |
| 43 | Open Domain Question Answering Over Tables Via Dense Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we tackle open-domain QA over tables for the first time, and show that retrieval can be improved by a retriever designed to handle tabular context. |
Jonathan Herzig; Thomas M�ller; Syrine Krichene; Julian Eisenschlos; |
| 44 | Open-Domain Question Answering Goes Conversational Via Question Rewriting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. |
Raviteja Anantha; Svitlana Vakulenko; Zhucheng Tu; Shayne Longpre; Stephen Pulman; Srinivas Chappidi; |
| 45 | QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we propose a new model, QA-GNN, which addresses the above challenges through two key innovations: (i) relevance scoring, where we use LMs to estimate the importance of KG nodes relative to the given QA context, and (ii) joint reasoning, where we connect the QA context and KG to form a joint graph, and mutually update their representations through graph-based message passing. |
Michihiro Yasunaga; Hongyu Ren; Antoine Bosselut; Percy Liang; Jure Leskovec; |
| 46 | XOR QA: Cross-lingual Open-Retrieval Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on this dataset, we introduce a task framework, called Cross-lingual Open-Retrieval Question Answering (XOR QA), that consists of three new tasks involving cross-lingual document retrieval from multilingual and English resources. |
Akari Asai; Jungo Kasai; Jonathan Clark; Kenton Lee; Eunsol Choi; Hannaneh Hajishirzi; |
| 47 | SPARTA: Efficient Open-Domain Question Answering Via Sparse Transformer Matching Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. |
Tiancheng Zhao; Xiaopeng Lu; Kyusong Lee; |
| 48 | Implicitly Abusive Language � What Does It Actually Look Like and Why Are We Not Getting There? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this position paper, we explain why existing datasets make learning implicit abuse difficult and what needs to be changed in the design of such datasets. |
Michael Wiegand; Josef Ruppenhofer; Elisabeth Eder; |
| 49 | The Importance of Modeling Social Factors of Language: Theory and Practice Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this position paper, we argue that the reason for the current limitations is a focus on information content while ignoring language’s social factors. |
Dirk Hovy; Diyi Yang; |
| 50 | On Learning and Representing Social Meaning in NLP: A Sociolinguistic Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the concept of social meaning to NLP and discuss how insights from sociolinguistics can inform work on representation learning in NLP. |
Dong Nguyen; Laura Rosseel; Jack Grieve; |
| 51 | Preregistering NLP Research Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The goal of this paper is to elicit a discussion in the NLP community, which we hope to synthesise into a general NLP preregistration form in future research. |
Emiel van Miltenburg; Chris van der Lee; Emiel Krahmer; |
| 52 | Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. |
Tal Schuster; Adam Fisch; Regina Barzilay; |
| 53 | Representing Numbers in NLP: A Survey and A Vision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We synthesize best practices for representing numbers in text and articulate a vision for holistic numeracy in NLP, comprised of design trade-offs and a unified evaluation. |
Avijit Thawani; Jay Pujara; Filip Ilievski; Pedro Szekely; |
| 54 | Extending Multi-Document Summarization Evaluation to The Interactive Setting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop an end-to-end evaluation framework for interactive summarization, focusing on expansion-based interaction, which considers the accumulating information along a user session. |
Ori Shapira; Ramakanth Pasunuru; Hadar Ronen; Mohit Bansal; Yael Amsterdamer; Ido Dagan; |
| 55 | Identifying Helpful Sentences in Product Reviews Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to maintain this advantage in situations where extreme brevity is needed, for example, when shopping by voice. |
Iftah Gamzu; Hila Gonen; Gilad Kutiel; Ran Levy; Eugene Agichtein; |
| 56 | Noisy Self-Knowledge Distillation for Text Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training on single reference and noisy datasets. |
Yang Liu; Sheng Shen; Mirella Lapata; |
| 57 | Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a novel and generalizable method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner. |
Alexander Fabbri; Simeng Han; Haoyuan Li; Haoran Li; Marjan Ghazvininejad; Shafiq Joty; Dragomir Radev; Yashar Mehdad; |
| 58 | Enhancing Factual Consistency of Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention. We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems. |
Chenguang Zhu; William Hinthorn; Ruochen Xu; Qingkai Zeng; Michael Zeng; Xuedong Huang; Meng Jiang; |
| 59 | Few-shot Intent Classification and Slot Filling with Retrieved Examples Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a span-level retrieval method that learns similar contextualized representations for spans with the same label via a novel batch-softmax objective. |
Dian Yu; Luheng He; Yuan Zhang; Xinya Du; Panupong Pasupat; Qi Li; |
| 60 | �Nice Try, Kiddo�: Investigating Ad Hominems in Dialogue Responses Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose categories of ad hominems, compose an annotated dataset, and build a classifier to analyze human and dialogue system responses to English Twitter posts. |
Emily Sheng; Kai-Wei Chang; Prem Natarajan; Nanyun Peng; |
| 61 | Human-like Informative Conversations: Better Acknowledgements Using Conditional Mutual Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work aims to build a dialogue agent that can weave new factual content into conversations as naturally as humans. |
Ashwin Paranjape; Christopher Manning; |
| 62 | A Comparative Study on Schema-Guided Dialogue State Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we conduct in-depth comparative studies to understand the use of natural language description for schema in dialog state tracking. |
Jie Cao; Yi Zhang; |
| 63 | Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel approach to model long-term slot context and to fully utilize the semantic correlation between slots and intents. |
Jie Wu; Ian Harris; Hongzhi Zhao; |
| 64 | How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a reinforcement learning system that (1) incorporates large-scale language modeling-based and commonsense reasoning-based pre-training to imbue the agent with relevant priors; and (2) leverages a factorized action space of action commands and dialogue, balancing between the two. |
Prithviraj Ammanabrolu; Jack Urbanek; Margaret Li; Arthur Szlam; Tim Rockt�schel; Jason Weston; |
| 65 | Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work focuses on linking to unseen KBs that do not have training data and whose schema is unknown during training. |
Yogarshi Vyas; Miguel Ballesteros; |
| 66 | Self-Training with Weak Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task. |
Giannis Karamanolakis; Subhabrata Mukherjee; Guoqing Zheng; Ahmed Hassan Awadallah; |
| 67 | Neural Language Modeling for Contextualized Temporal Graph Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents the first study on using large-scale pre-trained language models for automated generation of an event-level temporal graph for a document. |
Aman Madaan; Yiming Yang; |
| 68 | Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these shortcomings, we propose BEUrRE, a novel uncertain knowledge graph embedding method with calibrated probabilistic semantics. |
Xuelu Chen; Michael Boratko; Muhao Chen; Shib Sankar Dasgupta; Xiang Lorraine Li; Andrew McCallum; |
| 69 | Document-Level Event Argument Extraction By Conditional Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. |
Sha Li; Heng Ji; Jiawei Han; |
| 70 | Template Filling with Generative Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a framework based on end-to-end generative transformers for this task (i.e., GTT). |
Xinya Du; Alexander Rush; Claire Cardie; |
| 71 | Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that the words in the NLU training set can be modeled as a long-tailed distribution. |
Mengnan Du; Varun Manjunatha; Rajiv Jain; Ruchi Deshpande; Franck Dernoncourt; Jiuxiang Gu; Tong Sun; Xia Hu; |
| 72 | On Attention Redundancy: A Comprehensive Study Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the phase-independent and task-agnostic attention redundancy patterns, we propose a simple zero-shot pruning method as a case study. |
Yuchen Bian; Jiaji Huang; Xingyu Cai; Jiahong Yuan; Kenneth Church; |
| 73 | Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we design a battery of approaches intended to recover Personal Health Information (PHI) from a trained BERT. |
Eric Lehman; Sarthak Jain; Karl Pichotta; Yoav Goldberg; Byron Wallace; |
| 74 | Low-Complexity Probing Via Finding Subnetworks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We instead propose a subtractive pruning-based probe, where we find an existing subnetwork that performs the linguistic task of interest. |
Victor Sanh; Alexander Rush; |
| 75 | An Empirical Comparison of Instance Attribution Methods for NLP Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we evaluate the degree to which different potential instance attribution agree with respect to the importance of training samples. |
Pouya Pezeshkpour; Sarthak Jain; Byron Wallace; Sameer Singh; |
| 76 | Generalization in Instruction Following Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on instruction understanding in the blocks world domain and investigate the language understanding abilities of two top-performing systems for the task. |
Soham Dan; Michael Zhou; Dan Roth; |
| 77 | LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a simple yet highly effective approach, LightningDOT that accelerates the inference time of ITR by thousands of times, without sacrificing accuracy. |
Siqi Sun; Yen-Chun Chen; Linjie Li; Shuohang Wang; Yuwei Fang; Jingjing Liu; |
| 78 | Measuring Social Biases in Grounded Vision and Language Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the space of generalizations (Grounded-WEAT and Grounded-SEAT) and demonstrate that three generalizations answer different yet important questions about how biases, language, and vision interact. |
Candace Ross; Boris Katz; Andrei Barbu; |
| 79 | MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language Sequences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Modal-Temporal Attention Graph (MTAG). |
Jianing Yang; Yongxin Wang; Ruitao Yi; Yuying Zhu; Azaan Rehman; Amir Zadeh; Soujanya Poria; Louis-Philippe Morency; |
| 80 | Grounding Open-Domain Instructions to Automate Web Support Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a task and dataset to train AI agents from open-domain, step-by-step instructions originally written for people. |
Nancy Xu; Sam Masling; Michael Du; Giovanni Campagna; Larry Heck; James Landay; Monica Lam; |
| 81 | Modular Networks for Compositional Instruction Following Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals. |
Rodolfo Corona; Daniel Fried; Coline Devin; Dan Klein; Trevor Darrell; |
| 82 | Improving Cross-Modal Alignment in Vision Language Navigation Via Syntactic Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, in this paper, we propose a navigation agent that utilizes syntax information derived from a dependency tree to enhance alignment between the instruction and the current visual scenes. |
Jialu Li; Hao Tan; Mohit Bansal; |
| 83 | Improving Pretrained Models for Zero-shot Multi-label Text Classification Through Reinforced Label Hierarchy Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore to improve pretrained models with label hierarchies on the ZS-MTC task. |
Hui Liu; Danqing Zhang; Bing Yin; Xiaodan Zhu; |
| 84 | Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the problem of fine-tuning pre-trained LMs using only weak supervision, without any labeled data. |
Yue Yu; Simiao Zuo; Haoming Jiang; Wendi Ren; Tuo Zhao; Chao Zhang; |
| 85 | Posterior Differential Regularization with F-divergence for Improving Model Robustness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address the problem of enhancing model robustness through regularization. |
Hao Cheng; Xiaodong Liu; Lis Pereira; Yaoliang Yu; Jianfeng Gao; |
| 86 | Understanding Hard Negatives in Noise Contrastive Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop analytical tools to understand the role of hard negatives. |
Wenzheng Zhang; Karl Stratos; |
| 87 | Certified Robustness to Word Substitution Attack with Differential Privacy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose WordDP to achieve certified robustness against word substitution at- tacks in text classification via differential privacy (DP). |
Wenjie Wang; Pengfei Tang; Jian Lou; Li Xiong; |
| 88 | DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate these issues, we propose DReCA (Decomposing datasets into Reasoning Categories), a simple method for discovering and using latent reasoning categories in a dataset, to form additional high quality tasks. |
Shikhar Murty; Tatsunori Hashimoto; Christopher Manning; |
| 89 | Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that multilinguality is critical to making unsupervised systems practical for low-resource settings. |
Xavier Garcia; Aditya Siddhant; Orhan Firat; Ankur Parikh; |
| 90 | Macro-Average: Rare Types Are Important Too Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explore the simple type-based classifier metric, MacroF1, and study its applicability to MT evaluation. |
Thamme Gowda; Weiqiu You; Constantine Lignos; Jonathan May; |
| 91 | Assessing Reference-Free Peer Evaluation for Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We analyze various potential weaknesses of the approach, and find that it is surprisingly robust and likely to offer reasonable performance across a broad spectrum of domains and different system qualities. |
Sweta Agrawal; George Foster; Markus Freitag; Colin Cherry; |
| 92 | The Curious Case of Hallucinations in Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies. |
Vikas Raunak; Arul Menezes; Marcin Junczys-Dowmunt; |
| 93 | Towards Continual Learning for Multilingual Machine Translation Via Vocabulary Substitution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models, paving the way towards efficient continual learning for multilingual machine translation. |
Xavier Garcia; Noah Constant; Ankur Parikh; Orhan Firat; |
| 94 | Towards Modeling The Style of Translators in Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate methods to augment the state of the art Transformer model with translator information that is available in part of the training data. |
Yue Wang; Cuong Hoang; Marcello Federico; |
| 95 | Self-Supervised Test-Time Learning for Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we consider the task of unsupervised reading comprehension and present a method that performs test-time learning (TTL) on a given context (text passage), without requiring training on large-scale human-authored datasets containing context-question-answer triplets. |
Pratyay Banerjee; Tejas Gokhale; Chitta Baral; |
| 96 | Capturing Row and Column Semantics in Transformer Based Question Answering Over Tables Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose two novel approaches demonstrating that one can achieve superior performance on table QA task without even using any of these specialized pre-training techniques. |
Michael Glass; Mustafa Canim; Alfio Gliozzo; Saneem Chemmengath; Vishwajeet Kumar; Rishav Chakravarti; Avi Sil; Feifei Pan; Samarth Bharadwaj; Nicolas Rodolfo Fauceglia; |
| 97 | Explainable Multi-hop Verbal Reasoning Through Internal Monologue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the Explainable multi-hop Verbal Reasoner (EVR) to solve these limitations by (a) decomposing multi-hop reasoning problems into several simple ones, and (b) using natural language to guide the intermediate reasoning hops. |
Zhengzhong Liang; Steven Bethard; Mihai Surdeanu; |
| 98 | Robust Question Answering Through Sub-part Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To make a more robust and understandable QA system, we model question answering as an alignment problem. |
Jifan Chen; Greg Durrett; |
| 99 | Text Modular Networks: Learning to Decompose Tasks in The Language of Existing Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a general framework called Text Modular Networks(TMNs) for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. |
Tushar Khot; Daniel Khashabi; Kyle Richardson; Peter Clark; Ashish Sabharwal; |
| 100 | RECONSIDER: Improved Re-Ranking Using Span-Focused Cross-Attention for Open Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop a successful re-ranking approach (RECONSIDER) for span-extraction tasks that improves upon the performance of MRC models, even beyond large-scale pre-training. |
Srinivasan Iyer; Sewon Min; Yashar Mehdad; Wen-tau Yih; |
| 101 | On The Transferability of Minimal Prediction Preserving Inputs in Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the context of question answering, we investigate competing hypotheses for the existence of MPPIs, including poor posterior calibration of neural models, lack of pretraining, and dataset bias (where a model learns to attend to spurious, non-generalizable cues in the training data). |
Shayne Longpre; Yi Lu; Chris DuBois; |
| 102 | Understanding By Understanding Not: Modeling Negation in Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To improve language models in this regard, we propose to augment the language modeling objective with an unlikelihood objective that is based on negated generic sentences from a raw text corpus. |
Arian Hosseini; Siva Reddy; Dzmitry Bahdanau; R Devon Hjelm; Alessandro Sordoni; Aaron Courville; |
| 103 | DuoRAT: Towards Simpler Text-to-SQL Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Contrary to this trend, in this paper we focus on simplifications. |
Torsten Scholak; Raymond Li; Dzmitry Bahdanau; Harm de Vries; Chris Pal; |
| 104 | Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we use the multiple-choice reading comprehension (MCRC) and checking factual correctness of textual summarization (CFCS) tasks to investigate potential reasons for this. |
Anshuman Mishra; Dhruvesh Patel; Aparna Vijayakumar; Xiang Lorraine Li; Pavan Kapanipathi; Kartik Talamadupula; |
| 105 | Structure-Grounded Pretraining for Text-to-SQL Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (STRUG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus. |
Xiang Deng; Ahmed Hassan Awadallah; Christopher Meek; Oleksandr Polozov; Huan Sun; Matthew Richardson; |
| 106 | Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we define a new task in the NLP domain, incremental few-shot text classification, where the system incrementally handles multiple rounds of new classes. |
Congying Xia; Wenpeng Yin; Yihao Feng; Philip Yu; |
| 107 | Temporal Reasoning on Implicit Events from Distant Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we propose a neuro-symbolic temporal reasoning model, SymTime, which exploits distant supervision signals from large-scale text and uses temporal rules to combine start times and durations to infer end times. |
Ben Zhou; Kyle Richardson; Qiang Ning; Tushar Khot; Ashish Sabharwal; Dan Roth; |
| 108 | Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present ParaBART, a semantic sentence embedding model that learns to disentangle semantics and syntax in sentence embeddings obtained by pre-trained language models. |
James Y. Huang; Kuan-Hao Huang; Kai-Wei Chang; |
| 109 | Structure-Aware Abstractive Conversation Summarization Via Discourse and Action Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose to explicitly model the rich structures in conversations for more precise and accurate conversation summarization, by first incorporating discourse relations between utterances and action triples (who-doing-what) in utterances through structured graphs to better encode conversations, and then designing a multi-granularity decoder to generate summaries by combining all levels of information. |
Jiaao Chen; Diyi Yang; |
| 110 | A New Approach to Overgenerating and Scoring Abstractive Summaries Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users’ needs. |
Kaiqiang Song; Bingqing Wang; Zhe Feng; Fei Liu; |
| 111 | D2S: Document-to-Slide Generation Via Query-Based Text Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years’ NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach: |
Edward Sun; Yufang Hou; Dakuo Wang; Yunfeng Zhang; Nancy X. R. Wang; |
| 112 | Efficient Attentions for Long Document Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. |
Luyang Huang; Shuyang Cao; Nikolaus Parulian; Heng Ji; Lu Wang; |
| 113 | RefSum: Refactoring Neural Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we highlight several limitations of previous methods, which motivates us to present a new framework Refactor that provides a unified view of text summarization and summaries combination. |
Yixin Liu; Zi-Yi Dou; Pengfei Liu; |
| 114 | Annotating and Modeling Fine-grained Factuality in Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explore both synthetic and human-labeled data sources for training models to identify factual errors in summarization, and study factuality at the word-, dependency-, and sentence-level. |
Tanya Goyal; Greg Durrett; |
| 115 | Larger-Context Tagging: When and Why Does It Work? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, instead of pursuing a state-of-the-art tagging system by architectural exploration, we focus on investigating when and why the larger-context training, as a general strategy, can work. |
Jinlan Fu; Liangjing Feng; Qi Zhang; Xuanjing Huang; Pengfei Liu; |
| 116 | Neural Sequence Segmentation As Determining The Leftmost Segments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel framework that incrementally segments natural language sentences at segment level. |
Yangming Li; Lemao Liu; Kaisheng Yao; |
| 117 | PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a new parameterization form of PCFGs based on tensor decomposition, which has at most quadratic computational complexity in the symbol number and therefore allows us to use a much larger number of symbols. |
Songlin Yang; Yanpeng Zhao; Kewei Tu; |
| 118 | GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose GEMNET, a novel approach for gazetteer knowledge integration, including (1) a flexible Contextual Gazetteer Representation (CGR) encoder that can be fused with any word-level model; and (2) a Mixture-of- Experts gating network that overcomes the feature overuse issue by learning to conditionally combine the context and gazetteer features, instead of assigning them fixed weights. |
Tao Meng; Anjie Fang; Oleg Rokhlenko; Shervin Malmasi; |
| 119 | Video-aided Unsupervised Grammar Induction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore rich features (e.g. action, object, scene, audio, face, OCR and speech) from videos, taking the recent Compound PCFG model as the baseline. |
Songyang Zhang; Linfeng Song; Lifeng Jin; Kun Xu; Dong Yu; Jiebo Luo; |
| 120 | Generating Negative Samples By Manipulating Golden Responses for Unsupervised Learning of A Response Evaluation Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For the unsupervised learning of such model, we propose a method of manipulating a golden response to create a new negative response that is designed to be inappropriate within the context while maintaining high similarity with the original golden response. |
ChaeHun Park; Eugene Jang; Wonsuk Yang; Jong Park; |
| 121 | How Robust Are Fact Checking Systems on Colloquial Claims? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we ask: How robust are fact checking systems on claims in colloquial style? |
Byeongchang Kim; Hyunwoo Kim; Seokhee Hong; Gunhee Kim; |
| 122 | Fine-grained Post-training for Improving Retrieval-based Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a new fine-grained post-training method that reflects the characteristics of the multi-turn dialogue. |
Janghoon Han; Taesuk Hong; Byoungjae Kim; Youngjoong Ko; Jungyun Seo; |
| 123 | Put Chatbot Into Its Interlocutor�s Shoes: New Framework to Learn Chatbot Responding with Intention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an innovative framework to train chatbots to possess human-like intentions. |
Hsuan Su; Jiun-Hao Jhan; Fan-yun Sun; Saurav Sahay; Hung-yi Lee; |
| 124 | Adding Chit-Chat to Enhance Task-Oriented Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to integrate both types of systems by Adding Chit-Chat to ENhance Task-ORiented dialogues (ACCENTOR), with the goal of making virtual assistant conversations more engaging and interactive. |
Kai Sun; Seungwhan Moon; Paul Crook; Stephen Roller; Becka Silvert; Bing Liu; Zhiguang Wang; Honglei Liu; Eunjoon Cho; Claire Cardie; |
| 125 | Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a heterogeneous graph-based model to incorporate syntactic and semantic structures of sentences. |
Fan Jiang; Trevor Cohn; |
| 126 | Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we thus propose a novel graph-based Context Tracking Network (CT-Net) to model the discourse context for IDRR. |
Yingxue Zhang; Fandong Meng; Peng Li; Ping Jian; Jie Zhou; |
| 127 | Improving Neural RST Parsing Model with Silver Agreement Subtrees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose a method for improving neural RST parsing models by exploiting silver data, i.e., automatically annotated data. |
Naoki Kobayashi; Tsutomu Hirao; Hidetaka Kamigaito; Manabu Okumura; Masaaki Nagata; |
| 128 | RST Parsing from Scratch Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a novel top-down end-to-end formulation of document level discourse parsing in the Rhetorical Structure Theory (RST) framework. |
Thanh-Tung Nguyen; Xuan-Phi Nguyen; Shafiq Joty; Xiaoli Li; |
| 129 | Did They Answer? Subjective Acts and Intents in Conversational Discourse Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present the first discourse dataset with multiple and subjective interpretations of English conversation in the form of perceived conversation acts and intents. |
Elisa Ferracane; Greg Durrett; Junyi Jessy Li; Katrin Erk; |
| 130 | Evaluating The Impact of A Hierarchical Discourse Representation on Entity Coreference Resolution Performance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we leverage automatically constructed discourse parse trees within a neural approach and demonstrate a significant improvement on two benchmark entity coreference-resolution datasets. |
Sopan Khosla; James Fiacco; Carolyn Ros�; |
| 131 | Bridging Resolution: Making Sense of The State of The Art Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model. |
Hideo Kobayashi; Vincent Ng; |
| 132 | Explicitly Modeling Syntax in Language Models with Incremental Parsing and A Dynamic Oracle Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). |
Yikang Shen; Shawn Tan; Alessandro Sordoni; Siva Reddy; Aaron Courville; |
| 133 | Revisiting The Weaknesses of Reinforcement Learning for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit these claims and study them under a wider range of configurations. |
Samuel Kiegeland; Julia Kreutzer; |
| 134 | Learning to Organize A Bag of Words Into Sentences with Neural Networks: An Empirical Study Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by such an intuition, in this paper, we perform a study to investigate how order information takes effects in natural language learning. |
Chongyang Tao; Shen Gao; Juntao Li; Yansong Feng; Dongyan Zhao; Rui Yan; |
| 135 | Mask Attention Networks: Rethinking and Strengthen Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel understanding of SAN and FFN as Mask Attention Networks (MANs) and show that they are two special cases of MANs with static mask matrices. |
Zhihao Fan; Yeyun Gong; Dayiheng Liu; Zhongyu Wei; Siyuan Wang; Jian Jiao; Nan Duan; Ruofei Zhang; Xuanjing Huang; |
| 136 | ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As an alternative, we propose ERNIE-Gram, an explicitly n-gram masking method to enhance the integration of coarse-grained information into pre-training. |
Dongling Xiao; Yu-Kun Li; Han Zhang; Yu Sun; Hao Tian; Hua Wu; Haifeng Wang; |
| 137 | Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel pre-training paradigm for Chinese – Lattice-BERT, which explicitly incorporates word representations along with characters, thus can model a sentence in a multi-granularity manner. |
Yuxuan Lai; Yijia Liu; Yansong Feng; Songfang Huang; Dongyan Zhao; |
| 138 | Modeling Event Plausibility with Consistent Conceptual Abstraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We find this inconsistency persists even when models are softly injected with lexical knowledge, and we present a simple post-hoc method of forcing model consistency that improves correlation with human plausibility judgements. |
Ian Porada; Kaheer Suleman; Adam Trischler; Jackie Chi Kit Cheung; |
| 139 | UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using The Unified Medical Language System Metathesaurus Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce UmlsBERT, a contextual embedding model that integrates domain knowledge during the pre-training process via a novel knowledge augmentation strategy. |
George Michalopoulos; Yuanxin Wang; Hussam Kaka; Helen Chen; Alexander Wong; |
| 140 | Field Embedding: A Unified Grain-Based Framework for Word Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work proposes a framework field embedding to jointly learn both word and grain embeddings by incorporating morphological, phonetic, and syntactical linguistic fields. |
Junjie Luo; Xi Chen; Jichao Sun; Yuejia Xiang; Ningyu Zhang; Xiang Wan; |
| 141 | MelBERT: Metaphor Detection Via Contextualized Late Interaction Using Metaphorical Identification Theories Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel metaphor detection model, namely metaphor-aware late interaction over BERT (MelBERT). |
Minjin Choi; Sunkyung Lee; Eunseong Choi; Heesoo Park; Junhyuk Lee; Dongwon Lee; Jongwuk Lee; |
| 142 | Non-Parametric Few-Shot Learning for Word Sense Disambiguation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose MetricWSD, a non-parametric few-shot learning approach to mitigate this data imbalance issue. |
Howard Chen; Mengzhou Xia; Danqi Chen; |
| 143 | Why Do Document-Level Polarity Classifiers Fail? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We quantify the number of hard instances in polarity classification of movie reviews and provide empirical evidence about the need to pay attention to such problematic instances, as they are much harder to classify, for both machine and human classifiers. |
Karen Martins; Pedro O.S Vaz-de-Melo; Rodrygo Santos; |
| 144 | A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, motivated by its span-based representations of opinion expressions and roles, we propose a unified span-based approach for the end-to-end OM setting. |
Qingrong Xia; Bo Zhang; Rui Wang; Zhenghua Li; Yue Zhang; Fei Huang; Luo Si; Min Zhang; |
| 145 | Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Target-Specified sequence labeling with Multi-head Self-Attention (TSMSA) for TOWE, in which any pre-trained language model with multi-head self-attention can be integrated conveniently. |
Yuhao Feng; Yanghui Rao; Yuyao Tang; Ninghua Wang; He Liu; |
| 146 | Does Syntax Matter? A Strong Baseline for Aspect-based Sentiment Analysis with RoBERTa Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we firstly compare the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa (FT-RoBERTa) outperforms the parser-provided tree. |
Junqi Dai; Hang Yan; Tianxiang Sun; Pengfei Liu; Xipeng Qiu; |
| 147 | Domain Divergences: A Survey and Empirical Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop a taxonomy of divergence measures consisting of three classes – Information-theoretic, Geometric, and Higher-order measures and identify the relationships between them. |
Abhinav Ramesh Kashyap; Devamanyu Hazarika; Min-Yen Kan; Roger Zimmermann; |
| 148 | Target-Aware Data Augmentation for Stance Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formulate the data augmentation of stance detection as a conditional masked language modeling task and augment the dataset by predicting the masked word conditioned on both its context and the auxiliary sentence that contains target and label information. |
Yingjie Li; Cornelia Caragea; |
| 149 | End-to-end ASR to Jointly Predict Transcriptions and Linguistic Annotations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a Transformer-based sequence-to-sequence model for automatic speech recognition (ASR) capable of simultaneously transcribing and annotating audio with linguistic information such as phonemic transcripts or part-of-speech (POS) tags. |
Motoi Omachi; Yuya Fujita; Shinji Watanabe; Matthew Wiesner; |
| 150 | Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on sequence-level knowledge distillation (SeqKD) from external text-based NMT models. |
Hirofumi Inaguma; Tatsuya Kawahara; Shinji Watanabe; |
| 151 | Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present an end-to-end framework that exploits compositionality to learn searchable hidden representations at intermediate stages of a sequence model using decomposed sub-tasks. |
Siddharth Dalmia; Brian Yan; Vikas Raunak; Florian Metze; Shinji Watanabe; |
| 152 | SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules. |
Yu-An Chung; Chenguang Zhu; Michael Zeng; |
| 153 | Worldly Wise (WoW) – Cross-Lingual Knowledge Fusion for Fact-based Visual Spoken-Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Towards these ends, we present a new task and a synthetically-generated dataset to do Fact-based Visual Spoken-Question Answering (FVSQA). |
Kiran Ramnath; Leda Sari; Mark Hasegawa-Johnson; Chang Yoo; |
| 154 | Align-Refine: Non-Autoregressive Speech Recognition Via Iterative Realignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We instead propose *iterative realignment*, which by refining latent alignments allows more flexible edits in fewer steps. |
Ethan A. Chi; Julian Salazar; Katrin Kirchhoff; |
| 155 | Everything Has A Cause: Leveraging Causal Inference in Legal Text Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Graph-based Causal Inference (GCI) framework, which builds causal graphs from fact descriptions without much human involvement and enables causal inference to facilitate legal practitioners to make proper decisions. |
Xiao Liu; Da Yin; Yansong Feng; Yuting Wu; Dongyan Zhao; |
| 156 | Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose a counterfactual multi-granularity graph supporting facts extraction (CMGE) method to extract supporting facts from irregular EMR itself without external knowledge bases in this paper. |
Haoran Wu; Wei Chen; Shuang Xu; Bo Xu; |
| 157 | Personalized Response Generation Via Generative Split Memory Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we look at how to generate personalized responses for questions on Reddit by utilizing personalized user profiles and posting histories. |
Yuwei Wu; Xuezhe Ma; Diyi Yang; |
| 158 | Towards Few-shot Fact-Checking Via Perplexity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new way of utilizing the powerful transfer learning ability of a language model via a perplexity score. |
Nayeon Lee; Yejin Bang; Andrea Madotto; Pascale Fung; |
| 159 | Active2 Learning: Actively Reducing Redundancies in Active Learning Methods for Sequence Tagging and Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that since AL strategies choose examples independently, they may potentially select similar examples, all of which may not contribute significantly to the learning process. |
Rishi Hazra; Parag Dutta; Shubham Gupta; Mohammed Abdul Qaathir; Ambedkar Dukkipati; |
| 160 | Generating An Optimal Interview Question Plan Using A Knowledge Graph And Integer Linear Programming Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an interview assistant system to automatically, and in an objective manner, select an optimal set of technical questions (from question banks) personalized for a candidate. |
Soham Datta; Prabir Mallick; Sangameshwar Patil; Indrajit Bhattacharya; Girish Palshikar; |
| 161 | Model Extraction and Adversarial Transferability, Your BERT Is Vulnerable! Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we first present how an adversary can steal a BERT-based API service (the victim/target model) on multiple benchmark datasets with limited prior knowledge and queries. We further show that the extracted model can lead to highly transferable adversarial attacks against the victim model. |
Xuanli He; Lingjuan Lyu; Lichao Sun; Qiongkai Xu; |
| 162 | A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel Past-Future method to make comprehensive predictions from a global perspective. |
Kaiyuan Liao; Yi Zhang; Xuancheng Ren; Qi Su; Xu Sun; Bin He; |
| 163 | Masked Conditional Random Fields for Sequence Labeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose Masked Conditional Random Field (MCRF), an easy to implement variant of CRF that impose restrictions on candidate paths during both training and decoding phases. |
Tianwen Wei; Jianwei Qi; Shenghuan He; Songtao Sun; |
| 164 | Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel concept prerequisite relation learning approach, named CPRL, which combines both concept representation learned from a heterogeneous graph and concept pairwise features. |
Chenghao Jia; Yongliang Shen; Yechun Tang; Lu Sun; Weiming Lu; |
| 165 | Be Careful About Poisoned Word Embeddings: Exploring The Vulnerability of The Embedding Layers in NLP Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, in this paper, we find that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples. |
Wenkai Yang; Lei Li; Zhiyuan Zhang; Xuancheng Ren; Xu Sun; Bin He; |
| 166 | DA-Transformer: Distance-aware Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose DA-Transformer, which is a distance-aware Transformer that can exploit the real distance. |
Chuhan Wu; Fangzhao Wu; Yongfeng Huang; |
| 167 | ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the problem and advance related researches, we present a large-scale Chinese restaurant review dataset ASAP including 46, 730 genuine reviews from a leading online-to-offline (O2O) e-commerce platform in China. |
Jiahao Bu; Lei Ren; Shuang Zheng; Yang Yang; Jingang Wang; Fuzheng Zhang; Wei Wu; |
| 168 | Are NLP Models Really Able to Solve Simple Math Word Problems? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we restrict our attention to English MWPs taught in grades four and lower. |
Arkil Patel; Satwik Bhattamishra; Navin Goyal; |
| 169 | WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. |
Tomoyuki Kajiwara; Chenhui Chu; Noriko Takemura; Yuta Nakashima; Hajime Nagahara; |
| 170 | KPQA: A Metric for Generative Question Answering Using Keyphrase Weights Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this problem, we propose KPQA metric, a new metric for evaluating the correctness of GenQA. |
Hwanhee Lee; Seunghyun Yoon; Franck Dernoncourt; Doo Soon Kim; Trung Bui; Joongbo Shin; Kyomin Jung; |
| 171 | StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a large-scale benchmark, StylePTB, with (1) paired sentences undergoing 21 fine-grained stylistic changes spanning atomic lexical, syntactic, semantic, and thematic transfers of text, as well as (2) compositions of multiple transfers which allow modeling of fine-grained stylistic changes as building blocks for more complex, high-level transfers. |
Yiwei Lyu; Paul Pu Liang; Hai Pham; Eduard Hovy; Barnab�s P�czos; Ruslan Salakhutdinov; Louis-Philippe Morency; |
| 172 | Blow The Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a large and diverse Chinese dataset for creating and understanding cant from a computational linguistics perspective. |
Canwen Xu; Wangchunshu Zhou; Tao Ge; Ke Xu; Julian McAuley; Furu Wei; |
| 173 | COVID-19 Named Entity Recognition for Vietnamese Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present the first manually-annotated COVID-19 domain-specific dataset for Vietnamese. |
Thinh Hung Truong; Mai Hoang Dao; Dat Quoc Nguyen; |
| 174 | Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper addresses both issues with a novel semi-supervised model, which jointly learns to embed local information about the events and related actors in a news article through an auto-encoding framework, and to leverage this signal for document-level frame classification. |
Shima Khanehzar; Trevor Cohn; Gosia Mikolajczak; Andrew Turpin; Lea Frermann; |
| 175 | Automatic Classification of Neutralization Techniques in The Narrative of Climate Change Scepticism Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We first draw on social science to introduce the problem to the community of nlp, present the granularity of the coding schema and then collect manual annotations of neutralised techniques in text relating to climate change, and experiment with supervised and semi- supervised BERT-based models. |
Shraey Bhatia; Jey Han Lau; Timothy Baldwin; |
| 176 | Suicide Ideation Detection Via Social and Temporal User Representations Using Hyperbolic Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a framework jointly leveraging a user’s emotional history and social information from a user’s neighborhood in a network to contextualize the interpretation of the latest tweet of a user on Twitter. |
Ramit Sawhney; Harshit Joshi; Rajiv Ratn Shah; Lucie Flek; |
| 177 | WikiTalkEdit: A Dataset for Modeling Editors� Behaviors on Wikipedia Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study introduces and analyzes WikiTalkEdit, a dataset of conversations and edit histories from Wikipedia, for research in online cooperation and conversation modeling. |
Kokil Jaidka; Andrea Ceolin; Iknoor Singh; Niyati Chhaya; Lyle Ungar; |
| 178 | The Structure of Online Social Networks Modulates The Rate of Lexical Change Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We conduct a large-scale analysis of over 80k neologisms in 4420 online communities across a decade. |
Jian Zhu; David Jurgens; |
| 179 | Modeling Framing in Immigration Discourse on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By creating a new dataset of immigration-related tweets labeled for multiple framing typologies from political communication theory, we develop supervised models to detect frames. |
Julia Mendelsohn; Ceren Budak; David Jurgens; |
| 180 | Modeling The Severity of Complaints in Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the severity level of complaints for the first time in computational linguistics. |
Mali Jin; Nikolaos Aletras; |
| 181 | What About The Precedent: An Information-Theoretic Analysis of Common Law Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We are the first to approach this question computationally by comparing two longstanding jurisprudential views; Halsbury’s, who believes that the arguments of the precedent are the main determinant of the outcome, and Goodhart’s, who believes that what matters most is the precedent’s facts. |
Josef Valvoda; Tiago Pimentel; Niklas Stoehr; Ryan Cotterell; Simone Teufel; |
| 182 | Introducing CAD: The Contextual Abuse Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new dataset of primarily English Reddit entries which addresses several limitations of prior work. |
Bertie Vidgen; Dong Nguyen; Helen Margetts; Patricia Rossini; Rebekah Tromble; |
| 183 | Lifelong Learning of Hate Speech Classification on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose lifelong learning of hate speech classification on social media. |
Jing Qian; Hong Wang; Mai ElSherief; Xifeng Yan; |
| 184 | Learning to Recognize Dialect Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the task of dialect feature detection, and present two multitask learning approaches, both based on pretrained transformers. |
Dorottya Demszky; Devyani Sharma; Jonathan Clark; Vinodkumar Prabhakaran; Jacob Eisenstein; |
| 185 | It�s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that performance similar to GPT-3 can be obtained with language models that are much greener in that their parameter count is several orders of magnitude smaller. |
Timo Schick; Hinrich Sch�tze; |
| 186 | Static Embeddings As Efficient Knowledge Bases? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that, when restricting the output space to a candidate set, simple nearest neighbor matching using static embeddings performs better than PLMs. |
Philipp Dufter; Nora Kassner; Hinrich Sch�tze; |
| 187 | Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a simple yet effective KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches, while producing competitive performance. |
Xutan Peng; Guanyi Chen; Chenghua Lin; Mark Stevenson; |
| 188 | Rethinking Network Pruning � Under The Pre-train and Fine-tune Paradigm Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to fill this gap by studying how knowledge are transferred and lost during the pre-train, fine-tune, and pruning process, and proposing a knowledge-aware sparse pruning process that achieves significantly superior results than existing literature. |
Dongkuan Xu; Ian En-Hsu Yen; Jinxi Zhao; Zhibin Xiao; |
| 189 | Towards A Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate gender and racial bias across ubiquitous pre-trained language models, including GPT-2, XLNet, BERT, RoBERTa, ALBERT and DistilBERT. |
Andrew Silva; Pradyumna Tambwekar; Matthew Gombolay; |
| 190 | Detoxifying Language Models Risks Marginalizing Minority Voices Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that these detoxification techniques hurt equity: they decrease the utility of LMs on language used by marginalized groups (e.g., African-American English and minority identity mentions). |
Albert Xu; Eshaan Pathak; Eric Wallace; Suchin Gururangan; Maarten Sap; Dan Klein; |
| 191 | HONEST: Measuring Hurtful Sentence Completion in Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a score to measure hurtful sentence completions in language models (HONEST). |
Debora Nozza; Federico Bianchi; Dirk Hovy; |
| 192 | EaSe: A Diagnostic Tool for VQA Based on Answer Diversity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample. |
Shailza Jolly; Sandro Pezzelle; Moin Nabi; |
| 193 | DeCEMBERT: Learning from Noisy Instructional Videos Via Dense Captions and Entropy Minimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose an improved video-and-language pre-training method that first adds automatically-extracted dense region captions from the video frames as auxiliary text input, to provide informative visual cues for learning better video and language associations. |
Zineng Tang; Jie Lei; Mohit Bansal; |
| 194 | Improving Generation and Evaluation of Visual Stories Via Semantic Consistency Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a number of improvements to prior modeling approaches, including (1) the addition of a dual learning framework that utilizes video captioning to reinforce the semantic alignment between the story and generated images, (2) a copy-transform mechanism for sequentially-consistent story visualization, and (3) MART-based transformers to model complex interactions between frames. |
Adyasha Maharana; Darryl Hannan; Mohit Bansal; |
| 195 | Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextual multilingual multimodal embeddings. |
Po-Yao Huang; Mandela Patrick; Junjie Hu; Graham Neubig; Florian Metze; Alexander Hauptmann; |
| 196 | Video Question Answering with Phrases Via Semantic Roles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we leverage semantic roles derived from video descriptions to mask out certain phrases, to introduce VidQAP which poses VidQA as a fill-in-the-phrase task. |
Arka Sadhu; Kan Chen; Ram Nevatia; |
| 197 | From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. |
Rob van der Goot; Ibrahim Sharaf; Aizhan Imankulova; Ahmet �st�n; Marija Stepanovic; Alan Ramponi; Siti Oryza Khairunnisa; Mamoru Komachi; Barbara Plank; |
| 198 | WEC: Deriving A Large-scale Cross-document Event Coreference Dataset from Wikipedia Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To complement these resources and enhance future research, we present Wikipedia Event Coreference (WEC), an efficient methodology for gathering a large-scale dataset for cross-document event coreference from Wikipedia, where coreference links are not restricted within predefined topics. |
Alon Eirew; Arie Cattan; Ido Dagan; |
| 199 | Challenging Distributional Models with A Conceptual Network of Philosophical Terms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate the possibilities and limitations of using distributional semantic models for analyzing philosophical data by means of a realistic use-case. |
Yvette Oortwijn; Jelke Bloem; Pia Sommerauer; Francois Meyer; Wei Zhou; Antske Fokkens; |
| 200 | KILT: A Benchmark for Knowledge Intensive Language Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To catalyze research on models that condition on specific information in large textual resources, we present a benchmark for knowledge-intensive language tasks (KILT). |
Fabio Petroni; Aleksandra Piktus; Angela Fan; Patrick Lewis; Majid Yazdani; Nicola De Cao; James Thorne; Yacine Jernite; Vladimir Karpukhin; Jean Maillard; Vassilis Plachouras; Tim Rockt�schel; Sebastian Riedel; |
| 201 | A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific low-resource setting. |
Michael A. Hedderich; Lukas Lange; Heike Adel; Jannik Str�tgen; Dietrich Klakow; |
| 202 | Temporal Knowledge Graph Completion Using A Linear Temporal Regularizer and Multivector Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel time-aware knowledge graph embebdding approach, TeLM, which performs 4th-order tensor factorization of a Temporal knowledge graph using a Linear temporal regularizer and Multivector embeddings. |
Chengjin Xu; Yung-Yu Chen; Mojtaba Nayyeri; Jens Lehmann; |
| 203 | UDALM: Unsupervised Domain Adaptation Through Language Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language models for downstream tasks. |
Constantinos Karouzos; Georgios Paraskevopoulos; Alexandros Potamianos; |
| 204 | Beyond Black & White: Leveraging Annotator Disagreement Via Soft-Label Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel method to incorporate this disagreement as information: in addition to the standard error computation, we use soft-labels (i.e., probability distributions over the annotator labels) as an auxiliary task in a multi-task neural network. |
Tommaso Fornaciari; Alexandra Uma; Silviu Paun; Barbara Plank; Dirk Hovy; Massimo Poesio; |
| 205 | Clustering-based Inference for Biomedical Entity Linking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions. |
Rico Angell; Nicholas Monath; Sunil Mohan; Nishant Yadav; Andrew McCallum; |
| 206 | Variance-reduced First-order Meta-learning for Natural Language Processing Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, to address the overfitting issue when applying first-order meta-learning to NLP applications, we propose to reduce the variance of the gradient estimator used in task adaptation. |
Lingxiao Wang; Kevin Huang; Tengyu Ma; Quanquan Gu; Jing Huang; |
| 207 | Diversity-Aware Batch Active Learning for Dependency Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we attempt to reduce the number of labeled examples needed to train a strong dependency parser using batch active learning (AL). |
Tianze Shi; Adrian Benton; Igor Malioutov; Ozan Irsoy; |
| 208 | How Many Data Points Is A Prompt Worth? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We aim to quantify this benefit through rigorous testing of prompts in a fair setting: comparing prompted and head-based fine-tuning in equal conditions across many tasks and data sizes. |
Teven Le Scao; Alexander Rush; |
| 209 | Can Latent Alignments Improve Autoregressive Machine Translation? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explore the possibility of training autoregressive machine translation models with latent alignment objectives, and observe that, in practice, this approach results in degenerate models. |
Adi Haviv; Lior Vassertail; Omer Levy; |
| 210 | Smoothing and Shrinking The Sparse Seq2Seq Search Space Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that entmax-based models effectively solve the cat got your tongue problem, removing a major source of model error for neural machine translation. |
Ben Peters; Andr� F. T. Martins; |
| 211 | Unified Pre-training for Program Understanding and Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces PLBART, a sequence-to-sequence model capable of performing a broad spectrum of program and language understanding and generation tasks. |
Wasi Ahmad; Saikat Chakraborty; Baishakhi Ray; Kai-Wei Chang; |
| 212 | Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a hyperparameter-free continual learning model for text data that can stably produce high performance under various environments. |
Ting Hua; Yilin Shen; Changsheng Zhao; Yen-Chang Hsu; Hongxia Jin; |
| 213 | On The Embeddings of Variables in Recurrent Neural Networks for Source Code Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop dynamic embeddings, a recurrent mechanism that adjusts the learned semantics of the variable when it obtains more information about the variable’s role in the program. |
Nadezhda Chirkova; |
| 214 | Cross-Lingual Word Embedding Refinement By L1 Norm Optimisation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the more robust Manhattan norm (aka. l1 norm) goodness-of-fit criterion, this paper proposes a simple post-processing step to improve CLWEs. |
Xutan Peng; Chenghua Lin; Mark Stevenson; |
| 215 | Semantic Frame Forecast Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces Semantic Frame Forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. |
Chieh-Yang Huang; Ting-Hao Huang; |
| 216 | MUSER: MUltimodal Stress Detection Using Emotion Recognition As An Auxiliary Task Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the value of emotion recognition as an auxiliary task to improve stress detection. |
Yiqun Yao; Michalis Papakostas; Mihai Burzo; Mohamed Abouelenien; Rada Mihalcea; |
| 217 | Learning to Decompose and Organize Complex Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus in this paper, we propose a novel end-to-end pipeline that consumes a complex task and induces a dependency graph from unstructured text to represent sub-tasks and their relationships. |
Yi Zhang; Sujay Kumar Jauhar; Julia Kiseleva; Ryen White; Dan Roth; |
| 218 | Continual Learning for Text Classification with Information Disentanglement Based Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an information disentanglement based regularization method for continual learning on text classification. |
Yufan Huang; Yanzhe Zhang; Jiaao Chen; Xuezhi Wang; Diyi Yang; |
| 219 | Learning from Executions for Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on the task of semi-supervised learning where a limited amount of annotated data is available together with many unlabeled NL utterances. |
Bailin Wang; Mirella Lapata; Ivan Titov; |
| 220 | Learning to Synthesize Data for Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a generative model which features a (non-neural) PCFG that models the composition of programs (e.g., SQL), and a BART-based translation model that maps a program to an utterance. |
Bailin Wang; Wenpeng Yin; Xi Victoria Lin; Caiming Xiong; |
| 221 | Edge: Enriching Knowledge Graph Embeddings with External Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Previous work has partially addressed this issue by enriching knowledge graph entities based on hard co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve soft augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. |
Saed Rezayi; Handong Zhao; Sungchul Kim; Ryan Rossi; Nedim Lipka; Sheng Li; |
| 222 | FLIN: A Flexible Natural Language Interface for Web Navigation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose FLIN, a natural language interface for web navigation that maps user commands to concept-level actions (rather than low-level UI actions), thus being able to flexibly adapt to different websites and handle their transient nature. |
Sahisnu Mazumder; Oriana Riva; |
| 223 | Game-theoretic Vocabulary Selection Via The Shapley Value and Banzhaf Index Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a vocabulary selection method that views words as members of a team trying to maximize the model’s performance. |
Roma Patel; Marta Garnelo; Ian Gemp; Chris Dyer; Yoram Bachrach; |
| 224 | Incorporating External Knowledge to Enhance Tabular Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study these challenges through the problem of tabular natural language inference. |
J. Neeraja; Vivek Gupta; Vivek Srikumar; |
| 225 | Compositional Generalization for Neural Semantic Parsing Via Span-level Supervised Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers. |
Pengcheng Yin; Hao Fang; Graham Neubig; Adam Pauls; Emmanouil Antonios Platanios; Yu Su; Sam Thomson; Jacob Andreas; |
| 226 | Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new unsupervised domain adaptation method for Arabic cross-domain and cross-dialect sentiment analysis from Contextualized Word Embedding. |
Abdellah El Mekki; Abdelkader El Mahdaouy; Ismail Berrada; Ahmed Khoumsi; |
| 227 | Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a multi-task learning method to incorporate information from syntactic and semantic auxiliary tasks, including negation and speculation scope detection, to create English-language models that are more robust to these phenomena. |
Andrew Moore; Jeremy Barnes; |
| 228 | A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones. |
Gabriele Pergola; Lin Gui; Yulan He; |
| 229 | Graph Ensemble Learning Over Multiple Dependency Trees for Aspect-level Sentiment Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better leverage syntactic information in the face of unavoidable errors, we propose a simple yet effective graph ensemble technique, GraphMerge, to make use of the predictions from different parsers. |
Xiaochen Hou; Peng Qi; Guangtao Wang; Rex Ying; Jing Huang; Xiaodong He; Bowen Zhou; |
| 230 | Emotion-Infused Models for Explainable Psychological Stress Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we present work exploring the use of a semantically related task, emotion detection, for equally competent but more explainable and human-like psychological stress detection as compared to a black-box model. |
Elsbeth Turcan; Smaranda Muresan; Kathleen McKeown; |
| 231 | Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address such limitations, in this paper, we propose an approach to explicitly utilize dependency types for ABSA with type-aware graph convolutional networks (T-GCN), where attention is used in T-GCN to distinguish different edges (relations) in the graph and attentive layer ensemble is proposed to comprehensively learn from different layers of T-GCN. |
Yuanhe Tian; Guimin Chen; Yan Song; |
| 232 | Supertagging-based Parsing with Linear Context-free Rewriting Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present the first supertagging-based parser for linear context-free rewriting systems (LCFRS). |
Thomas Ruprecht; Richard M�rbitz; |
| 233 | Outside Computation with Superior Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that a general algorithm for efficient computation of outside values under the minimum of superior functions framework proposed by Knuth (1977) would yield a sub-exponential time algorithm for SAT, violating the Strong Exponential Time Hypothesis (SETH). |
Parker Riley; Daniel Gildea; |
| 234 | Learning Syntax from Naturally-Occurring Bracketings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: But they are noisy and incomplete; to address this challenge, we develop a partial-brackets-aware structured ramp loss in learning. |
Tianze Shi; Ozan Irsoy; Igor Malioutov; Lillian Lee; |
| 235 | Bot-Adversarial Dialogue for Safe Conversational Agents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new human-and-model-in-the-loop framework for evaluating the toxicity of such models, and compare a variety of existing methods in both the cases of non-adversarial and adversarial users that expose their weaknesses. |
Jing Xu; Da Ju; Margaret Li; Y-Lan Boureau; Jason Weston; Emily Dinan; |
| 236 | Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture. |
Arun Babu; Akshat Shrivastava; Armen Aghajanyan; Ahmed Aly; Angela Fan; Marjan Ghazvininejad; |
| 237 | Example-Driven Intent Prediction with Observers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the intent classification problem which aims to identify user intents given utterances addressed to the dialog system. |
Shikib Mehri; Mihail Eric; |
| 238 | Imperfect Also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a multi-level reward modeling approach that factorizes a reward into a three-level hierarchy: domain, act, and slot. |
Zhengxu Hou; Bang Liu; Ruihui Zhao; Zijing Ou; Yafei Liu; Xi Chen; Yefeng Zheng; |
| 239 | Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To study customer service dialogue systems in more realistic settings, we introduce the Action-Based Conversations Dataset (ABCD), a fully-labeled dataset with over 10K human-to-human dialogues containing 55 distinct user intents requiring unique sequences of actions constrained by policies to achieve task success. |
Derek Chen; Howard Chen; Yi Yang; Alexander Lin; Zhou Yu; |
| 240 | Controlling Dialogue Generation with Semantic Exemplars Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an Exemplar-based Dialogue Generation model, EDGE, that uses the semantic frames present in exemplar responses to guide response generation. |
Prakhar Gupta; Jeffrey Bigham; Yulia Tsvetkov; Amy Pavel; |
| 241 | COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents COIL, a contextualized exact match retrieval architecture, where scoring is based on overlapping query document tokens’ contextualized representations. |
Luyu Gao; Zhuyun Dai; Jamie Callan; |
| 242 | X-Class: Text Classification with Extremely Weak Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore text classification with extremely weak supervision, i.e., only relying on the surface text of class names. |
Zihan Wang; Dheeraj Mekala; Jingbo Shang; |
| 243 | Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, we propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual neural topic modeling. |
Aaron Mueller; Mark Dredze; |
| 244 | Exploring The Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this gap, we provide a comprehensive study that examines how preprocessing techniques affect the vocabulary size, model performance, and model run-time, evaluating ten techniques over four models and two datasets. |
Wilson Fearn; Orion Weller; Kevin Seppi; |
| 245 | Faithfully Explainable Recommendation Via Neural Logic Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose neural logic reasoning for explainable recommendation (LOGER) by drawing on interpretable logical rules to guide the path-reasoning process for explanation generation. |
Yaxin Zhu; Yikun Xian; Zuohui Fu; Gerard de Melo; Yongfeng Zhang; |
| 246 | You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explore one promising direction for conversational recommendation: mapping a conversational user, for whom there is limited or no data available, to most similar external reviewers, whose preferences are known, by representing the conversation as a user’s interest vector, and adapting collaborative filtering techniques to estimate the current user’s preferences for new movies. |
Sergey Volokhin; Joyce Ho; Oleg Rokhlenko; Eugene Agichtein; |
| 247 | Reading and Acting While Blindfolded: The Need for Semantics in Text Game Agents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To remedy this deficiency, we propose an inverse dynamics decoder to regularize the representation space and encourage exploration, which shows improved performance on several games including Zork I. |
Shunyu Yao; Karthik Narasimhan; Matthew Hausknecht; |
| 248 | SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we first present a gradient-based interpretability approach to determine the questions most strongly correlated with the reasoning question on an image, and use this to evaluate VQA models on their ability to identify the relevant sub-questions needed to answer a reasoning question. |
Sameer Dharur; Purva Tendulkar; Dhruv Batra; Devi Parikh; Ramprasaath R. Selvaraju; |
| 249 | Semi-Supervised Policy Initialization for Playing Games with Language Hints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose semi-supervised initialization (SSI) that allows the agent to learn from various possible hints before training under different tasks. |
Tsu-Jui Fu; William Yang Wang; |
| 250 | Revisiting Document Representations for Large-Scale Zero-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit the use of documents as semantic representations. |
Jihyung Kil; Wei-Lun Chao; |
| 251 | Negative Language Transfer in Learner English: A New Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a learner English dataset in which learner errors are accompanied by information about possible error sources. |
Leticia Farias Wanderley; Nicole Zhao; Carrie Demmans Epp; |
| 252 | SentSim: Crosslingual Semantic Evaluation of Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a more cost-effective, yet well performing unsupervised alternative SentSim: relying on strong pretrained multilingual word and sentence representations, we directly compare the source with the machine translated sentence, thus avoiding the need for both reference translations and labelled training data. |
Yurun Song; Junchen Zhao; Lucia Specia; |
| 253 | Quality Estimation for Image Captions Based on Large-scale Human Evaluations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the task of Quality Estimation (QE) for image captions, which attempts to model the caption quality from a human perspective and *without* access to ground-truth references, so that it can be applied at prediction time to detect low-quality captions produced on *previously unseen images*. |
Tomer Levinboim; Ashish V. Thapliyal; Piyush Sharma; Radu Soricut; |
| 254 | CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English. |
Kushal Chawla; Jaysa Ramirez; Rene Clever; Gale Lucas; Jonathan May; Jonathan Gratch; |
| 255 | News Headline Grouping As A Challenging NLU Task Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the task of HeadLine Grouping (HLG) and a corresponding dataset (HLGD) consisting of 20,056 pairs of news headlines, each labeled with a binary judgement as to whether the pair belongs within the same group. |
Philippe Laban; Lucas Bandarkar; Marti A. Hearst; |
| 256 | Ol�, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We take the first step towards multilingual style transfer by creating and releasing XFORMAL, a benchmark of multiple formal reformulations of informal text in Brazilian Portuguese, French, and Italian. |
Eleftheria Briakou; Di Lu; Ke Zhang; Joel Tetreault; |
| 257 | Grouping Words with Semantic Diversity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce an approach by grouping input words based on their semantic diversity to simplify input language representation with low ambiguity. |
Karine Chubarian; Abdul Rafae Khan; Anastasios Sidiropoulos; Jia Xu; |
| 258 | Noise Stability Regularization for Improving BERT Fine-tuning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically,we introduce a novel and effective regulariza-tion method to improve fine-tuning on NLPtasks, referred to asLayer-wiseNoiseStabilityRegularization (LNSR). |
Hang Hua; Xingjian Li; Dejing Dou; Chengzhong Xu; Jiebo Luo; |
| 259 | FlowPrior: Learning Expressive Priors for Latent Variable Sentence Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We find that existing training strategies are not effective for learning rich priors, so we propose adding the importance-sampled log marginal likelihood as a second term to the standard VAE objective to help when learning the prior. |
Xiaoan Ding; Kevin Gimpel; |
| 260 | HTCInfoMax: A Global Model for Hierarchical Text Classification Via Information Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose HTCInfoMax to address these issues by introducing information maximization which includes two modules: text-label mutual information maximization and label prior matching. |
Zhongfen Deng; Hao Peng; Dongxiao He; Jianxin Li; Philip Yu; |
| 261 | Knowledge Guided Metric Learning for Few-Shot Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge. |
Dianbo Sui; Yubo Chen; Binjie Mao; Delai Qiu; Kang Liu; Jun Zhao; |
| 262 | Ensemble of MRR and NDCG Models for Visual Dialog Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we describe a two-step non-parametric ranking approach that can merge strong MRR and NDCG models. |
Idan Schwartz; |
| 263 | Supervised Neural Clustering Via Latent Structured Output Learning: Application to Question Intents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we design neural networks based on latent structured prediction loss and Transformer models to approach supervised clustering. |
Iryna Haponchyk; Alessandro Moschitti; |
| 264 | ConVEx: Data-Efficient and Few-Shot Slot Labeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose ConVEx (Conversational Value Extractor), an efficient pretraining and fine-tuning neural approach for slot-labeling dialog tasks. |
Matthew Henderson; Ivan Vulic; |
| 265 | CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding. |
Bo-Hsiang Tseng; Shruti Bhargava; Jiarui Lu; Joel Ruben Antony Moniz; Dhivya Piraviperumal; Lin Li; Hong Yu; |
| 266 | Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formalize knowledge-driven slot constraints and present a new task of constraint violation detection accompanied with benchmarking data. |
Piyawat Lertvittayakumjorn; Daniele Bonadiman; Saab Mansour; |
| 267 | Clipping Loops for Sample-Efficient Dialogue Policy Optimisation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose loop-clipping policy optimisation (LCPO) to eliminate useless responses. |
Yen-Chen Wu; Carl Edward Rasmussen; |
| 268 | Integrating Lexical Information Into Entity Neighbourhood Representations for Relation Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an extension of OpenKi that incorporates embeddings of text-based representations of the entities and the relations. |
Ian Wood; Mark Johnson; Stephen Wan; |
| 269 | Noisy-Labeled NER with Confidence Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on empirical observations of different training dynamics of noisy and clean labels, we propose strategies for estimating confidence scores based on local and global independence assumptions. |
Kun Liu; Yao Fu; Chuanqi Tan; Mosha Chen; Ningyu Zhang; Songfang Huang; Sheng Gao; |
| 270 | TABBIE: Pretrained Representations of Tabular Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We devise a simple pretraining objective (corrupt cell detection) that learns exclusively from tabular data and reaches the state-of-the-art on a suite of table-based prediction tasks. |
Hiroshi Iida; Dung Thai; Varun Manjunatha; Mohit Iyyer; |
| 271 | Better Feature Integration for Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a simple and robust solution to incorporate both types of features with our Synergized-LSTM (Syn-LSTM), which clearly captures how the two types of features interact. |
Lu Xu; Zhanming Jie; Wei Lu; Lidong Bing; |
| 272 | ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formulate the zero-shot relation extraction problem by incorporating the text description of seen and unseen relations. |
Chih-Yao Chen; Cheng-Te Li; |
| 273 | Graph Convolutional Networks for Event Causality Identification with Rich Document-level Structures Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As such, we propose a graph-based model that constructs interaction graphs to capture relevant connections between important objects for DECI in input documents. |
Minh Tran Phu; Thien Huu Nguyen; |
| 274 | A Context-Dependent Gated Module for Incorporating Symbolic Semantics Into Event Coreference Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by these observations, we propose a novel context-dependent gated module to adaptively control the information flows from the input symbolic features. |
Tuan Lai; Heng Ji; Trung Bui; Quan Hung Tran; Franck Dernoncourt; Walter Chang; |
| 275 | Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In our work, we relax this requirement of jointly annotated data across multiple styles by using independently acquired data across different style dimensions without any additional annotations. |
Navita Goyal; Balaji Vasan Srinivasan; Anandhavelu N; Abhilasha Sancheti; |
| 276 | FUDGE: Controlled Text Generation With Future Discriminators Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Future Discriminators for Generation (FUDGE), a flexible and modular method for controlled text generation. |
Kevin Yang; Dan Klein; |
| 277 | Controllable Text Simplification with Explicit Paraphrasing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles. |
Mounica Maddela; Fernando Alva-Manchego; Wei Xu; |
| 278 | Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, however, we verbalize the entire English Wikidata KG, and discuss the unique challenges associated with a broad, open-domain, large-scale verbalization. |
Oshin Agarwal; Heming Ge; Siamak Shakeri; Rami Al-Rfou; |
| 279 | Choose Your Own Adventure: Paired Suggestions in Collaborative Writing for Evaluating Story Generation Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Choose Your Own Adventure, a collaborative writing setup for pairwise model evaluation. |
Elizabeth Clark; Noah A. Smith; |
| 280 | InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present an information-theoretic framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts. |
Zewen Chi; Li Dong; Furu Wei; Nan Yang; Saksham Singhal; Wenhui Wang; Xia Song; Xian-Ling Mao; Heyan Huang; Ming Zhou; |
| 281 | Context-Interactive Pre-Training for Document Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To remedy this, here we propose a simple yet effective context-interactive pre-training approach, which targets benefiting from external large-scale corpora. |
Pengcheng Yang; Pei Zhang; Boxing Chen; Jun Xie; Weihua Luo; |
| 282 | Code-Mixing on Sesame Street: Dawn of The Adversarial Polyglots Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by this phenomenon, we present two strong black-box adversarial attacks (one word-level, one phrase-level) for multilingual models that push their ability to handle code-mixed sentences to the limit. |
Samson Tan; Shafiq Joty; |
| 283 | X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for NLU. |
Meryem M�hamdi; Doo Soon Kim; Franck Dernoncourt; Trung Bui; Xiang Ren; Jonathan May; |
| 284 | Explicit Alignment Objectives for Multilingual Bidirectional Encoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new method for learning multilingual encoders, AMBER (Aligned Multilingual Bidirectional EncodeR). |
Junjie Hu; Melvin Johnson; Orhan Firat; Aditya Siddhant; Graham Neubig; |
| 285 | Cross-lingual Cross-modal Pretraining for Multimodal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new approach to learn cross-lingual cross-modal representations for matching images and their relevant captions in multiple languages. |
Hongliang Fei; Tan Yu; Ping Li; |
| 286 | Wikipedia Entities As Rendezvous Across Languages: Grounding Multilingual Language Models By Predicting Wikipedia Hyperlinks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a language-independent entity prediction task as an intermediate training procedure to ground word representations on entity semantics and bridge the gap across different languages by means of a shared vocabulary of entities. |
Iacer Calixto; Alessandro Raganato; Tommaso Pasini; |
| 287 | MultiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose two variants of a proof-set generation model, multiPRover. |
Swarnadeep Saha; Prateek Yadav; Mohit Bansal; |
| 288 | Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we develop a neural LM that includes an interpretable neuro-symbolic KB in the form of a fact memory. |
Pat Verga; Haitian Sun; Livio Baldini Soares; William Cohen; |
| 289 | CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions Over Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take visual understanding to a higher level where systems are challenged to answer questions that involve mentally simulating the hypothetical consequences of performing specific actions in a given scenario. |
Shailaja Keyur Sampat; Akshay Kumar; Yezhou Yang; Chitta Baral; |
| 290 | Refining Targeted Syntactic Evaluation of Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We argue that current implementations of TSE do not directly capture either of these goals, and propose new metrics to capture each goal separately. |
Benjamin Newman; Kai-Siang Ang; Julia Gong; John Hewitt; |
| 291 | Universal Adversarial Attacks with Natural Triggers for Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We leverage an adversarially regularized autoencoder (ARAE) to generate triggers and propose a gradient-based search that aims to maximize the downstream classifier’s prediction loss. |
Liwei Song; Xinwei Yu; Hsuan-Tung Peng; Karthik Narasimhan; |
| 292 | QuadrupletBERT: An Efficient Model For Embedding-Based Large-Scale Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Considering that pre-trained language models like BERT have achieved great success in a wide variety of NLP tasks, we present a QuadrupletBERT model for effective and efficient retrieval in this paper. |
Peiyang Liu; Sen Wang; Xi Wang; Wei Ye; Shikun Zhang; |
| 293 | Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an adversarial disentangled debiasing model to dynamically decouple social bias attributes from the intermediate representations trained on the main task. |
Liwen Wang; Yuanmeng Yan; Keqing He; Yanan Wu; Weiran Xu; |
| 294 | An Empirical Investigation of Bias in The Multimodal Analysis of Financial Earnings Calls Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present the first study to discover the gender bias in multimodal volatility prediction due to gender-sensitive audio features and fewer female executives in earnings calls of one of the world’s biggest stock indexes, the S&P 500 index. |
Ramit Sawhney; Arshiya Aggarwal; Rajiv Ratn Shah; |
| 295 | Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We find that the Final Rule, the common ethical framework used by researchers, did not anticipate the use of online crowdsourcing platforms for data collection, resulting in gaps between the spirit and practice of human-subjects ethics in NLP research. |
Boaz Shmueli; Jan Fell; Soumya Ray; Lun-Wei Ku; |
| 296 | On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explore the feasibility and benefits of upstream bias mitigation (UBM) for reducing bias on downstream tasks, by first applying bias mitigation to an upstream model through fine-tuning and subsequently using it for downstream fine-tuning. |
Xisen Jin; Francesco Barbieri; Brendan Kennedy; Aida Mostafazadeh Davani; Leonardo Neves; Xiang Ren; |
| 297 | Case Study: Deontological Ethics in NLP Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we study one ethical theory, namely deontological ethics, from the perspective of NLP. |
Shrimai Prabhumoye; Brendon Boldt; Ruslan Salakhutdinov; Alan W Black; |
| 298 | Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy through (1) the use of a discriminator and (2) the inclusion of a novel triplet-loss term. |
Fatemehsadat Mireshghallah; Huseyin Inan; Marcello Hasegawa; Victor R�hle; Taylor Berg-Kirkpatrick; Robert Sim; |
| 299 | On The Impact of Random Seeds on The Fairness of Clinical Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Recent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s). We explore the implications of this phenomenon for model fairness across demographic groups in clinical prediction tasks over electronic health records (EHR) in MIMIC-III — the standard dataset in clinical NLP research. |
Silvio Amir; Jan-Willem van de Meent; Byron Wallace; |
| 300 | Topic Model or Topic Twaddle? Re-evaluating Semantic Interpretability Measures Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we probe the issue of validity in topic model evaluation and assess how informative coherence measures are for specialized collections used in an applied setting. |
Caitlin Doogan; Wray Buntine; |
| 301 | Discourse Probing of Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce document-level discourse probing to evaluate the ability of pretrained LMs to capture document-level relations. |
Fajri Koto; Jey Han Lau; Timothy Baldwin; |
| 302 | UniDrop: A Simple Yet Effective Technique to Improve Transformer Without Extra Cost Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, we integrate different dropout techniques into the training of Transformer models. |
Zhen Wu; Lijun Wu; Qi Meng; Yingce Xia; Shufang Xie; Tao Qin; Xinyu Dai; Tie-Yan Liu; |
| 303 | TWT�WT: A Dataset to Assert The Role of Target Entities for Detecting Stance of Tweets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Though the task involves reasoning of the tweet with respect to a target, we find that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence. |
Ayush Kaushal; Avirup Saha; Niloy Ganguly; |
| 304 | Learning to Learn to Be Right for The Right Reasons Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose to explicitly learn a model that does well on both the easy test set with superficial cues and the hard test set without superficial cues. |
Pride Kavumba; Benjamin Heinzerling; Ana Brassard; Kentaro Inui; |
| 305 | Double Perturbation: On The Robustness of Robustness and Counterfactual Bias Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a double perturbation framework to uncover model weaknesses beyond the test dataset. |
Chong Zhang; Jieyu Zhao; Huan Zhang; Kai-Wei Chang; Cho-Jui Hsieh; |
| 306 | Explaining Neural Network Predictions on Sentence Pairs Via Learning Word-Group Masks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose the Group Mask (GMASK) method to implicitly detect word correlations by grouping correlated words from the input text pair together and measure their contribution to the corresponding NLP tasks as a whole. |
Hanjie Chen; Song Feng; Jatin Ganhotra; Hui Wan; Chulaka Gunasekara; Sachindra Joshi; Yangfeng Ji; |
| 307 | Almost Free Semantic Draft for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, to inject global information but also save cost, we present an efficient method to sample and consider a semantic draft as global information from semantic space for decoding with almost free of cost. |
Xi Ai; Bin Fang; |
| 308 | Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these three problems, we propose a method of ?divide and conquer? which is based on the importance of neurons or parameters for the translation model. |
Shuhao Gu; Yang Feng; Wanying Xie; |
| 309 | Multi-Hop Transformer for Document-Level Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel Multi-Hop Transformer (MHT) which offers NMT abilities to explicitly model the human-like draft-editing and reasoning process. |
Long Zhang; Tong Zhang; Haibo Zhang; Baosong Yang; Wei Ye; Shikun Zhang; |
| 310 | Continual Learning for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new continual learning framework for NMT models. |
Yue Cao; Hao-Ran Wei; Boxing Chen; Xiaojun Wan; |
| 311 | Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on this scenario, we propose UNMT self-training mechanisms to train a robust UNMT system and improve its performance in this case. |
Haipeng Sun; Rui Wang; Kehai Chen; Masao Utiyama; Eiichiro Sumita; Tiejun Zhao; |
| 312 | Smart-Start Decoding for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel method that breaks up the limitation of these decoding orders, called Smart-Start decoding. |
Jian Yang; Shuming Ma; Dongdong Zhang; Juncheng Wan; Zhoujun Li; Ming Zhou; |
| 313 | Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we hypothesize and empirically verify that AT and NAT encoders capture different linguistic properties of source sentences. |
Yongchang Hao; Shilin He; Wenxiang Jiao; Zhaopeng Tu; Michael Lyu; Xing Wang; |
| 314 | ER-AE: Differentially Private Text Generation for Authorship Anonymization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel text generation model with a two-set exponential mechanism for authorship anonymization. |
Haohan Bo; Steven H. H. Ding; Benjamin C. M. Fung; Farkhund Iqbal; |
| 315 | Distantly Supervised Transformers For E-Commerce Product QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a practical instant question answering (QA) system on product pages of e-commerce services, where for each user query, relevant community question answer (CQA) pairs are retrieved. |
Happy Mittal; Aniket Chakrabarti; Belhassen Bayar; Animesh Anant Sharma; Nikhil Rasiwasia; |
| 316 | Quantitative Day Trading from Natural Language Using Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Building on these shortcomings, we propose a deep reinforcement learning approach that makes time-aware decisions to trade stocks while optimizing profit using textual data. |
Ramit Sawhney; Arnav Wadhwa; Shivam Agarwal; Rajiv Ratn Shah; |
| 317 | Restoring and Mining The Records of The Joseon Dynasty Via Neural Language Modeling and Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In response, we present a multi-task learning approach to restore and translate historical documents based on a self-attention mechanism, specifically utilizing two Korean historical records, ones of the most voluminous historical records in the world. |
Kyeongpil Kang; Kyohoon Jin; Soyoung Yang; Soojin Jang; Jaegul Choo; Youngbin Kim; |
| 318 | Modeling Diagnostic Label Correlation for Automatic ICD Coding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a two-stage framework to improve automatic ICD coding by capturing the label correlation. |
Shang-Chi Tsai; Chao-Wei Huang; Yun-Nung Chen; |
| 319 | Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we suggest a self-supervised contrastive learning approach that leverages the pool of unlabeled data to learn user-agent interactions. |
Mohammad Kachuee; Hao Yuan; Young-Bum Kim; Sungjin Lee; |
| 320 | A Recipe for Annotating Grounded Clarifications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that dialogue clarification mechanisms make explicit the process of interpreting the communicative intents of the speaker’s utterances by grounding them in the various modalities in which the dialogue is situated. |
Luciana Benotti; Patrick Blackburn; |
| 321 | Grey-box Adversarial Attack And Defence For Sentiment Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a grey-box adversarial attack and defence framework for sentiment classification. |
Ying Xu; Xu Zhong; Antonio Jimeno Yepes; Jey Han Lau; |
| 322 | How Low Is Too Low? A Monolingual Take on Lemmatisation in Indian Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we devote our attention to lemmatisation for low resource, morphologically rich scheduled Indian languages using neural methods. |
Kumar Saunack; Kumar Saurav; Pushpak Bhattacharyya; |
| 323 | Causal Effects of Linguistic Properties Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on these results, we introduce TextCause, an algorithm for estimating causal effects of linguistic properties. |
Reid Pryzant; Dallas Card; Dan Jurafsky; Victor Veitch; Dhanya Sridhar; |
| 324 | Dynabench: Rethinking Benchmarking in NLP Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that Dynabench addresses a critical need in our community: contemporary models quickly achieve outstanding performance on benchmark tasks but nonetheless fail on simple challenge examples and falter in real-world scenarios. |
Douwe Kiela; Max Bartolo; Yixin Nie; Divyansh Kaushik; Atticus Geiger; Zhengxuan Wu; Bertie Vidgen; Grusha Prasad; Amanpreet Singh; Pratik Ringshia; Zhiyi Ma; Tristan Thrush; Sebastian Riedel; Zeerak Waseem; Pontus Stenetorp; Robin Jia; Mohit Bansal; Christopher Potts; Adina Williams; |
| 325 | Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that many significant advances in NLP research have emerged from the intersection of basic principles with application needs, and present a conceptual framework outlining the stakeholders and key questions in translational research. |
Denis Newman-Griffis; Jill Fain Lehman; Carolyn Ros�; Harry Hochheiser; |
| 326 | Predicting Discourse Trees from Transformer-based Neural Summarizers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by inferring document-level discourse trees from pre-trained neural summarizers. |
Wen Xiao; Patrick Huber; Giuseppe Carenini; |
| 327 | Probing for Bridging Inference in Transformer Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We first investigate individual attention heads in BERT and observe that attention heads at higher layers prominently focus on bridging relations in-comparison with the lower and middle layers, also, few specific attention heads concentrate consistently on bridging. |
Onkar Pandit; Yufang Hou; |
| 328 | Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Extending the targeted evaluation paradigm for neural language models (Marvin and Linzen, 2018) to phenomena beyond syntax, we show that this paradigm is equally suited to evaluate linguistic qualities that contribute to the notion of coherence. |
Anne Beyer; Sharid Lo�iciga; David Schlangen; |
| 329 | Stay Together: A System for Single and Split-antecedent Anaphora Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a system that resolves both single and split-antecedent anaphors, and evaluate it in a more realistic setting that uses predicted mentions. |
Juntao Yu; Nafise Sadat Moosavi; Silviu Paun; Massimo Poesio; |
| 330 | Redefining Absent Keyphrases and Their Effect on Retrieval Effectiveness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we discuss the usefulness of absent keyphrases from an Information Retrieval (IR) perspective, and show that the commonly drawn distinction between present and absent keyphrases is not made explicit enough. |
Florian Boudin; Ygor Gallina; |
| 331 | CoRT: Complementary Rankings from Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this context we propose CoRT, a simple neural first-stage ranking model that leverages contextual representations from pretrained language models such as BERT to complement term-based ranking functions while causing no significant delay at query time. |
Marco Wrzalik; Dirk Krechel; |
| 332 | Multi-source Neural Topic Modeling in Multi-view Embedding Spaces Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents a novel neural topic modeling framework using multi-view embed ding spaces: (1) pretrained topic-embeddings, and (2) pretrained word-embeddings (context-insensitive from Glove and context-sensitive from BERT models) jointly from one or many sources to improve topic quality and better deal with polysemy. |
Pankaj Gupta; Yatin Chaudhary; Hinrich Sch�tze; |
| 333 | Inductive Topic Variational Graph Auto-Encoder for Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose a novel model named inductive Topic Variational Graph Auto-Encoder (T-VGAE), which incorporates a topic model into variational graph-auto-encoder (VGAE) to capture the hidden semantic information between documents and words. |
Qianqian Xie; Jimin Huang; Pan Du; Min Peng; Jian-Yun Nie; |
| 334 | Self-Alignment Pretraining for Biomedical Entity Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. |
Fangyu Liu; Ehsan Shareghi; Zaiqiao Meng; Marco Basaldella; Nigel Collier; |
| 335 | TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore to conduct HMTC based on only class surface names as supervision signals. |
Jiaming Shen; Wenda Qiu; Yu Meng; Jingbo Shang; Xiang Ren; Jiawei Han; |
| 336 | MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. |
Tuhin Chakrabarty; Xurui Zhang; Smaranda Muresan; Nanyun Peng; |
| 337 | On Learning Text Style Transfer with Direct Rewards Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore training algorithms that instead optimize reward functions that explicitly consider different aspects of the style-transferred outputs. |
Yixin Liu; Graham Neubig; John Wieting; |
| 338 | Focused Attention Improves Document-Grounded Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our work introduces two novel adaptations of large scale pre-trained encoder-decoder models focusing on building context driven representation of the document and enabling specific attention to the information in the document. |
Shrimai Prabhumoye; Kazuma Hashimoto; Yingbo Zhou; Alan W Black; Ruslan Salakhutdinov; |
| 339 | NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose NeuroLogic Decoding, a simple yet effective algorithm that enables neural language models – supervised or not – to generate fluent text while satisfying complex lexical constraints. |
Ximing Lu; Peter West; Rowan Zellers; Ronan Le Bras; Chandra Bhagavatula; Yejin Choi; |
| 340 | Ask What�s Missing and What�s Useful: Improving Clarification Question Generation Using Global Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by this, we propose a model for clarification question generation where we first identify what is missing by taking a difference between the global and the local view and then train a model to identify what is useful and generate a question about it. |
Bodhisattwa Prasad Majumder; Sudha Rao; Michel Galley; Julian McAuley; |
| 341 | Progressive Generation of Long Text with Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the limitations, we propose a simple but effective method of generating text in a progressive manner, inspired by generating images from low to high resolution. |
Bowen Tan; Zichao Yang; Maruan Al-Shedivat; Eric Xing; Zhiting Hu; |
| 342 | SOCCER: An Information-Sparse Discourse State Tracking Collection in The Sports Commentary Domain Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes to turn to simplified, fully observable systems that show some of these properties: Sports events. |
Ruochen Zhang; Carsten Eickhoff; |
| 343 | Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to tackle these issues by generating a more comprehensive set of implausible stories using plots, which are structured representations of controllable factors used to generate stories. |
Sarik Ghazarian; Zixi Liu; Akash S M; Ralph Weischedel; Aram Galstyan; Nanyun Peng; |
| 344 | MultiOpEd: A Corpus of Multi-Perspective News Editorials Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose MultiOpEd, an open-domain news editorial corpus that supports various tasks pertaining to the argumentation structure in news editorials, focusing on automatic perspective discovery. |
Siyi Liu; Sihao Chen; Xander Uyttendaele; Dan Roth; |
| 345 | Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We release a new benchmark for lexical substitution, the task of finding appropriate substitutes for a target word in a context. |
Mina Lee; Chris Donahue; Robin Jia; Alexander Iyabor; Percy Liang; |
| 346 | �I�m Not Mad�: Commonsense Implications of Negation and Contradiction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions. |
Liwei Jiang; Antoine Bosselut; Chandra Bhagavatula; Yejin Choi; |
| 347 | Identifying Medical Self-Disclosure in Online Communities Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address this shortcoming by introducing a new dataset of health-related posts collected from online social platforms, categorized into three groups (No Self-Disclosure, Possible Self-Disclosure, and Clear Self-Disclosure) with high inter-annotator agreement (_k_=0.88). |
Mina Valizadeh; Pardis Ranjbar-Noiey; Cornelia Caragea; Natalie Parde; |
| 348 | Language in A (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines; in particular, we explore the emergence of semantic generalization from unsupervised dense representations outside of synthetic environments. |
Federico Bianchi; Ciro Greco; Jacopo Tagliabue; |
| 349 | Finding Concept-specific Biases in Form�Meaning Associations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents an information-theoretic operationalisation of cross-linguistic non-arbitrariness. |
Tiago Pimentel; Brian Roark; S�ren Wichmann; Ryan Cotterell; Dami�n Blasi; |
| 350 | How (Non-)Optimal Is The Lexicon? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Taking a coding-theoretic view of the lexicon and making use of a novel generative statistical model, we define upper bounds for the compressibility of the lexicon under various constraints. |
Tiago Pimentel; Irene Nikkarinen; Kyle Mahowald; Ryan Cotterell; Dami�n Blasi; |
| 351 | Word Complexity Is in The Eye of The Beholder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate which aspects contribute to the notion of lexical complexity in various groups of readers, focusing on native and non-native speakers of English, and how the notion of complexity changes depending on the proficiency level of a non-native reader. |
Sian Gooding; Ekaterina Kochmar; Seid Muhie Yimam; Chris Biemann; |
| 352 | Linguistic Complexity Loss in Text-Based Therapy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we analyze linguistic complexity correlates of mental health in the online therapy messages sent between therapists and 7,170 clients who provided 30,437 corresponding survey responses on their anxiety. |
Jason Wei; Kelly Finn; Emma Templeton; Thalia Wheatley; Soroush Vosoughi; |
| 353 | Ab Antiquo: Neural Proto-language Reconstruction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address the task of proto-word reconstruction, in which the model is exposed to cognates in contemporary daughter languages, and has to predict the proto word in the ancestor language. |
Carlo Meloni; Shauli Ravfogel; Yoav Goldberg; |
| 354 | On Biasing Transformer Attention Towards Monotonicity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a monotonicity loss function that is compatible with standard attention mechanisms and test it on several sequence-to-sequence tasks: grapheme-to-phoneme conversion, morphological inflection, transliteration, and dialect normalization. |
Annette Rios; Chantal Amrhein; No�mi Aepli; Rico Sennrich; |
| 355 | Extracting A Knowledge Base of Mechanisms from COVID-19 Papers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We pursue the construction of a knowledge base (KB) of mechanisms—a fundamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. |
Tom Hope; Aida Amini; David Wadden; Madeleine van Zuylen; Sravanthi Parasa; Eric Horvitz; Daniel Weld; Roy Schwartz; Hannaneh Hajishirzi; |
| 356 | Constrained Multi-Task Learning for Event Coreference Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a neural event coreference model in which event coreference is jointly trained with five tasks: trigger detection, entity coreference, anaphoricity determination, realis detection, and argument extraction. |
Jing Lu; Vincent Ng; |
| 357 | Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we provide a systematic study on the role of dimension reduction methods (principal components analysis, factorization techniques, or multi-layer auto-encoders) as well as the dimensionality of embedding vectors and sample sizes as a function of predictive performance. |
Adithya V Ganesan; Matthew Matero; Aravind Reddy Ravula; Huy Vu; H. Andrew Schwartz; |
| 358 | Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a novel method of using BERT-based hidden layer representations of clinical texts as covariates for proportional hazards models to predict patient survival outcomes. |
Hyun Gi Lee; Evan Sholle; Ashley Beecy; Subhi Al�Aref; Yifan Peng; |
| 359 | On The Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles. |
Rakesh Gosangi; Ravneet Arora; Mohsen Gheisarieha; Debanjan Mahata; Haimin Zhang; |
| 360 | Data and Model Distillation As A Solution for Domain-transferable Fact Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: While neural networks produce state-of-the-art performance in several NLP tasks, they generally depend heavily on lexicalized information, which transfer poorly between domains. We present a combination of two strategies to mitigate this dependence on lexicalized information in fact verification tasks |
Mitch Paul Mithun; Sandeep Suntwal; Mihai Surdeanu; |
| 361 | Adapting Coreference Resolution for Processing Violent Death Narratives Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we an-alyzed the challenges of coreference resolu-tion in an exemplary form of administrativetext written in English: violent death nar-ratives from the USA’s Centers for DiseaseControl’s (CDC) National Violent Death Re-porting System. |
Ankith Uppunda; Susan Cochran; Jacob Foster; Alina Arseniev-Koehler; Vickie Mays; Kai-Wei Chang; |
| 362 | Time-Stamped Language Model: Teaching Language Models to Understand The Flow of Events Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Tracking entities throughout a procedure described in a text is challenging due to the dynamic nature of the world described in the process. Firstly, we propose to formulate this task as a question answering problem. |
Hossein Rajaby Faghihi; Parisa Kordjamshidi; |
| 363 | If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To retrieve such facts, we propose a simple approach that retrieves and reranks set of evidence facts jointly. |
Vikas Yadav; Steven Bethard; Mihai Surdeanu; |
| 364 | SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM). |
Roshanak Mirzaee; Hossein Rajaby Faghihi; Qiang Ning; Parisa Kordjamshidi; |
| 365 | A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We therefore present Qasper, a dataset of 5049 questions over 1585 Natural Language Processing papers. |
Pradeep Dasigi; Kyle Lo; Iz Beltagy; Arman Cohan; Noah A. Smith; Matt Gardner; |
| 366 | Differentiable Open-Ended Commonsense Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As an approach to OpenCSR, we propose DrFact, an efficient Differentiable model for multi-hop Reasoning over knowledge Facts. |
Bill Yuchen Lin; Haitian Sun; Bhuwan Dhingra; Manzil Zaheer; Xiang Ren; William Cohen; |
| 367 | Does Structure Matter? Encoding Documents for Machine Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work proposes a new Transformer-based method that reads a document as tree slices. |
Hui Wan; Song Feng; Chulaka Gunasekara; Siva Sankalp Patel; Sachindra Joshi; Luis Lastras; |
| 368 | Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Building on dense retrieval methods, we propose a new multi-step retrieval approach (BeamDR) that iteratively forms an evidence chain through beam search in dense representations. |
Chen Zhao; Chenyan Xiong; Jordan Boyd-Graber; Hal Daum� III; |
| 369 | Scalable and Interpretable Semantic Change Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel scalable method for word usage-change detection that offers large gains in processing time and significant memory savings while offering the same interpretability and better performance than unscalable methods. |
Syrielle Montariol; Matej Martinc; Lidia Pivovarova; |
| 370 | Scalar Adjective Identification and Multilingual Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new multilingual dataset in order to promote research on scalar adjectives in new languages. |
Aina Gar� Soler; Marianna Apidianaki; |
| 371 | ESC: Redesigning WSD with Extractive Sense Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We cope with this issue by reframing WSD as a span extraction problem – which we called Extractive Sense Comprehension (ESC) – and propose ESCHER, a transformer-based neural architecture for this new formulation. |
Edoardo Barba; Tommaso Pasini; Roberto Navigli; |
| 372 | Recent Advances in Neural Metaphor Processing: A Linguistic, Cognitive and Social Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper provides a comprehensive review and discussion of recent developments in automated metaphor processing, in light of the findings about metaphor in the mind, language, and communication, and from the perspective of downstream NLP tasks. |
Xiaoyu Tong; Ekaterina Shutova; Martha Lewis; |
| 373 | Constructing Taxonomies from Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a method for constructing taxonomic trees (e.g., WordNet) using pretrained language models. |
Catherine Chen; Kevin Lin; Dan Klein; |
| 374 | Event Representation with Sequential, Semi-Supervised Discrete Variables Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. |
Mehdi Rezaee; Francis Ferraro; |
| 375 | Seq2Emo: A Sequence to Multi-Label Emotion Classification Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a sequence-to-emotion (Seq2Emo) approach, which implicitly models emotion correlations in a bi-directional decoder. |
Chenyang Huang; Amine Trabelsi; Xuebin Qin; Nawshad Farruque; Lili Mou; Osmar Za�ane; |
| 376 | Knowledge Enhanced Masked Language Model for Stance Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel BERT-based fine-tuning method that enhances the masked language model for stance detection. |
Kornraphop Kawintiranon; Lisa Singh; |
| 377 | Learning Paralinguistic Features from Audiobooks Through Style Voice Conversion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a new framework that enables a neural network to learn to extract paralinguistic attributes from speech using data that are not annotated for emotion. |
Zakaria Aldeneh; Matthew Perez; Emily Mower Provost; |
| 378 | Adapting BERT for Continual Learning of A Sequence of Aspect Sentiment Classification Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel capsule network based model called B-CL to address these issues. |
Zixuan Ke; Hu Xu; Bing Liu; |
| 379 | Adversarial Learning for Zero-Shot Stance Detection on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics. |
Emily Allaway; Malavika Srikanth; Kathleen McKeown; |
| 380 | Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents an efficient graph-enhanced approach to multi-document summarization (MDS) with an encoder-decoder Transformer model. |
Ramakanth Pasunuru; Mengwen Liu; Mohit Bansal; Sujith Ravi; Markus Dreyer; |
| 381 | Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we adapt TP-Transformer (Schlag et al., 2019), an architecture that enriches the original Transformer (Vaswani et al., 2017) with the explicitly compositional Tensor Product Representation (TPR), for the task of abstractive summarization. |
Yichen Jiang; Asli Celikyilmaz; Paul Smolensky; Paul Soulos; Sudha Rao; Hamid Palangi; Roland Fernandez; Caitlin Smith; Mohit Bansal; Jianfeng Gao; |
| 382 | What�s in A Summary? Laying The Groundwork for Advances in Hospital-Course Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we introduce the task of hospital-course summarization. |
Griffin Adams; Emily Alsentzer; Mert Ketenci; Jason Zucker; No�mie Elhadad; |
| 383 | Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these limitations, we devise a typology of factual errors and use it to collect human annotations of generated summaries from state-of-the-art summarization systems for the CNN/DM and XSum datasets. |
Artidoro Pagnoni; Vidhisha Balachandran; Yulia Tsvetkov; |
| 384 | GSum: A General Framework for Guided Neural Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a general and extensible guided summarization framework (GSum) that can effectively take different kinds of external guidance as input, and we perform experiments across several different varieties. |
Zi-Yi Dou; Pengfei Liu; Hiroaki Hayashi; Zhengbao Jiang; Graham Neubig; |
| 385 | What Will It Take to Fix Benchmarking in Natural Language Understanding? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this position paper, we lay out four criteria that we argue NLU benchmarks should meet. |
Samuel R. Bowman; George Dahl; |
| 386 | TuringAdvice: A Generative and Dynamic Evaluation of Language Use Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose TuringAdvice, a new challenge task and dataset for language understanding models. |
Rowan Zellers; Ari Holtzman; Elizabeth Clark; Lianhui Qin; Ali Farhadi; Yejin Choi; |
| 387 | Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We formulate the task of identifying narratives related to the sexual abuse disclosures in online posts as a joint modeling task that leverages their emotional attributes through multitask learning. |
Ramit Sawhney; Puneet Mathur; Taru Jain; Akash Kumar Gautam; Rajiv Ratn Shah; |
| 388 | Self Promotion in US Congressional Tweets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study we built a BERT-based NLP model to predict whether a Congressional tweet shows self-promotion or not and then used this model to examine whether a gender gap in self-promotion exists among Congressional tweets. |
Jun Wang; Kelly Cui; Bei Yu; |
| 389 | Profiling of Intertextuality in Latin Literature Using Word Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We report an empirical analysis of intertextuality in classical Latin literature using word embedding models. |
Patrick J. Burns; James Brofos; Kyle Li; Pramit Chaudhuri; Joseph P. Dexter; |
| 390 | Identifying Inherent Disagreement in Natural Language Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate how to tease systematic inferences (i.e., items for which people agree on the NLI label) apart from disagreement items (i.e., items which lead to different annotations), which most prior work has overlooked. |
Xinliang Frederick Zhang; Marie-Catherine de Marneffe; |
| 391 | Modeling Human Mental States with An Entity-based Narrative Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an Entity-based Narrative Graph (ENG) to model the internal- states of characters in a story. |
I-Ta Lee; Maria Leonor Pacheco; Dan Goldwasser; |
| 392 | A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we exploit labeled non-dialogue text data related to the condition, which are much easier to collect. |
Yan Zeng; Jian-Yun Nie; |
| 393 | Hurdles to Progress in Long-form Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: While many models have recently been proposed for LFQA, we show in this paper that the task formulation raises fundamental challenges regarding evaluation and dataset creation that currently preclude meaningful modeling progress. |
Kalpesh Krishna; Aurko Roy; Mohit Iyyer; |
| 394 | ENTRUST: Argument Reframing with Language Models and Entailment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Differences in lexical framing, the focus of our work, can have large effects on peoples’ opinions and beliefs. To make progress towards reframing arguments for positive effects, we create a dataset and method for this task. |
Tuhin Chakrabarty; Christopher Hidey; Smaranda Muresan; |
| 395 | Paragraph-level Simplification of Medical Texts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. |
Ashwin Devaraj; Iain Marshall; Byron Wallace; Junyi Jessy Li; |
| 396 | An Empirical Study on Neural Keyphrase Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this empirical study, we aim to fill this gap by providing extensive experimental results and analyzing the most crucial factors impacting the generalizability of KPG models. |
Rui Meng; Xingdi Yuan; Tong Wang; Sanqiang Zhao; Adam Trischler; Daqing He; |
| 397 | Attention Head Masking for Inference Time Content Selection in Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a simple-yet-effective attention head masking technique, which is applied on encoder-decoder attentions to pinpoint salient content at inference time. |
Shuyang Cao; Lu Wang; |
| 398 | Factual Probing Is [MASK]: Learning Vs. Learning to Recall Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we make two complementary contributions to better understand these factual probing techniques. |
Zexuan Zhong; Dan Friedman; Danqi Chen; |
| 399 | Evaluating Saliency Methods for Neural Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this question, we conduct a comprehensive and quantitative evaluation of saliency methods on a fundamental category of NLP models: neural language models. |
Shuoyang Ding; Philipp Koehn; |
| 400 | Contextualized Perturbation for Textual Adversarial Attack Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents CLARE, a ContextuaLized AdversaRial Example generation model that produces fluent and grammatical outputs through a mask-then-infill procedure. |
Dianqi Li; Yizhe Zhang; Hao Peng; Liqun Chen; Chris Brockett; Ming-Ting Sun; Bill Dolan; |
| 401 | DirectProbe: Studying Representations Without Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we argue that doing so can be unreliable because different representations may need different classifiers. |
Yichu Zhou; Vivek Srikumar; |
| 402 | Evaluating The Values of Sources in Transfer Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop , an efficient source valuation framework for quantifying the usefulness of the sources (e.g., ) in transfer learning based on the Shapley value method. |
Md Rizwan Parvez; Kai-Wei Chang; |
| 403 | Too Much in Common: Shifting of Embeddings in Transformer Language Models and Its Implications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We resolve this by showing, contrary to previous studies, that the representations do not occupy a narrow cone, but rather drift in common directions. |
Daniel Bis; Maksim Podkorytov; Xiuwen Liu; |
| 404 | On The Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To explain the empirical success of these generic masks, we demonstrate a correspondence between the Masked Language Model (MLM) objective and existing methods for learning statistical dependencies in graphical models. |
Tianyi Zhang; Tatsunori Hashimoto; |
| 405 | Limitations of Autoregressive Models and Their Alternatives Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, simply training larger autoregressive language models is not a panacea for NLP. Alternatives include energy-based models (which give up efficient sampling) and latent-variable autoregressive models (which give up efficient scoring of a given string). |
Chu-Cheng Lin; Aaron Jaech; Xin Li; Matthew R. Gormley; Jason Eisner; |
| 406 | On The Transformer Growth for Progressive BERT Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our objective is to help advance the understanding of such Transformer growth and discover principles that guide progressive training. |
Xiaotao Gu; Liyuan Liu; Hongkun Yu; Jing Li; Chen Chen; Jiawei Han; |
| 407 | Revisiting Simple Neural Probabilistic Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit the neural probabilistic language model (NPLM) of Bengio et al. (2003), which simply concatenates word embeddings within a fixed window and passes the result through a feed-forward network to predict the next word. |
Simeng Sun; Mohit Iyyer; |
| 408 | ReadTwice: Reading Very Large Documents with Memories Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose ReadTwice, a simple and effective technique that combines several strengths of prior approaches to model long-range dependencies with Transformers. |
Yury Zemlyanskiy; Joshua Ainslie; Michiel de Jong; Philip Pham; Ilya Eckstein; Fei Sha; |
| 409 | SCRIPT: Self-Critic PreTraining of Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce Self-CRItic Pretraining Transformers (SCRIPT) for representation learning of text. |
Erik Nijkamp; Bo Pang; Ying Nian Wu; Caiming Xiong; |
| 410 | Learning How to Ask: Querying LMs with Mixtures of Soft Prompts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We explore the idea of learning prompts by gradient descent-either fine-tuning prompts taken from previous work, or starting from random initialization. |
Guanghui Qin; Jason Eisner; |
| 411 | Nutri-bullets Hybrid: Consensual Multi-document Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a method for generating comparative summaries that highlight similarities and contradictions in input documents. |
Darsh Shah; Lili Yu; Tao Lei; Regina Barzilay; |
| 412 | AVA: An Automatic EValuation Approach for Question Answering Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce AVA, an automatic evaluation approach for Question Answering, which given a set of questions associated with Gold Standard answers (references), can estimate system Accuracy. |
Thuy Vu; Alessandro Moschitti; |
| 413 | SpanPredict: Extraction of Predictive Document Spans with Neural Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We here formalize this problem as predictive extraction and address it using a simple mechanism based on linear attention. |
Vivek Subramanian; Matthew Engelhard; Sam Berchuck; Liqun Chen; Ricardo Henao; Lawrence Carin; |
| 414 | Text Editing By Command Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel text editing task, and introduce WikiDocEdits, a dataset of single-sentence edits crawled from Wikipedia. |
Felix Faltings; Michel Galley; Gerold Hintz; Chris Brockett; Chris Quirk; Jianfeng Gao; Bill Dolan; |
| 415 | A Deep Metric Learning Approach to Account Linking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the task of linking social media accounts that belong to the same author in an automated fashion on the basis of the content and meta-data of the corresponding document streams. |
Aleem Khan; Elizabeth Fleming; Noah Schofield; Marcus Bishop; Nicholas Andrews; |
| 416 | Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we introduce two new simple rewards to encourage the generation of factually complete and consistent radiology reports: one that encourages the system to generate radiology domain entities consistent with the reference, and one that uses natural language inference to encourage these entities to be described in inferentially consistent ways. |
Yasuhide Miura; Yuhao Zhang; Emily Tsai; Curtis Langlotz; Dan Jurafsky; |
| 417 | Multimodal End-to-End Sparse Model for Emotion Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a fully end-to-end model that connects the two phases and optimizes them jointly. |
Wenliang Dai; Samuel Cahyawijaya; Zihan Liu; Pascale Fung; |
| 418 | MIMOQA: Multimodal Input Multimodal Output Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel task – MIMOQA – Multimodal Input Multimodal Output Question Answering in which the output is also multimodal. |
Hrituraj Singh; Anshul Nasery; Denil Mehta; Aishwarya Agarwal; Jatin Lamba; Balaji Vasan Srinivasan; |
| 419 | OCID-Ref: A 3D Robotic Dataset With Embodied Language For Clutter Scene Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In our work, we propose a novel OCID-Ref dataset featuring a referring expression segmentation task with referring expressions of occluded objects. |
Ke-Jyun Wang; Yun-Hsuan Liu; Hung-Ting Su; Jen-Wei Wang; Yu-Siang Wang; Winston Hsu; Wen-Chin Chen; |
| 420 | Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we propose to conduct mask-and-predict pre-training on text-only and image-only corpora and introduce the object tags detected by an object recognition model as anchor points to bridge two modalities. |
Liunian Harold Li; Haoxuan You; Zhecan Wang; Alireza Zareian; Shih-Fu Chang; Kai-Wei Chang; |
| 421 | Multitasking Inhibits Semantic Drift Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the dynamics of learning in latent language policies (LLPs), in which instructor agents generate natural-language subgoal descriptions and executor agents map these descriptions to low-level actions. |
Athul Paul Jacob; Mike Lewis; Jacob Andreas; |
| 422 | Probing Contextual Language Models for Common Ground with Visual Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned with corresponding visual representations? |
Gabriel Ilharco; Rowan Zellers; Ali Farhadi; Hannaneh Hajishirzi; |
| 423 | BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose BBAEG (Biomedical BERT-based Adversarial Example Generation), a black-box attack algorithm for biomedical text classification, leveraging the strengths of both domain-specific synonym replacement for biomedical named entities and BERT-MLM predictions, spelling variation and number replacement. |
Ishani Mondal; |
| 424 | Targeted Adversarial Training for Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a simple yet effective Targeted Adversarial Training (TAT) algorithm to improve adversarial training for natural language understanding. |
Lis Pereira; Xiaodong Liu; Hao Cheng; Hoifung Poon; Jianfeng Gao; Ichiro Kobayashi; |
| 425 | Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a generalized latent optimization strategy that allows different losses to accommodate each other and improves training dynamics. |
Xu Guo; Boyang Li; Han Yu; Chunyan Miao; |
| 426 | Self-training Improves Pre-training for Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study self-training as another way to leverage unlabeled data through semi-supervised learning. |
Jingfei Du; Edouard Grave; Beliz Gunel; Vishrav Chaudhary; Onur Celebi; Michael Auli; Veselin Stoyanov; Alexis Conneau; |
| 427 | Supporting Clustering with Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose Supporting Clustering with Contrastive Learning (SCCL) – a novel framework to leverage contrastive learning to promote better separation. |
Dejiao Zhang; Feng Nan; Xiaokai Wei; Shang-Wen Li; Henghui Zhu; Kathleen McKeown; Ramesh Nallapati; Andrew O. Arnold; Bing Xiang; |
| 428 | TITA: A Two-stage Interaction and Topic-Aware Text Matching Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the problem of keyword and document matching by considering different relevance levels. |
Xingwu Sun; Yanling Cui; Hongyin Tang; Qiuyu Zhu; Fuzheng Zhang; Beihong Jin; |
| 429 | Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents the Neural Verification Network (VERNet) for GEC quality estimation with multiple hypotheses. |
Zhenghao Liu; Xiaoyuan Yi; Maosong Sun; Liner Yang; Tat-Seng Chua; |
| 430 | Neural Network Surgery: Injecting Data Patterns Into Pre-trained Models with Minimal Instance-wise Side Effects Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by neuroscientific evidence and theoretical results, we demonstrate that side effects can be controlled by the number of changed parameters and thus, we propose to conduct neural network surgery by only modifying a limited number of parameters. |
Zhiyuan Zhang; Xuancheng Ren; Qi Su; Xu Sun; Bin He; |
| 431 | Discrete Argument Representation Learning for Interactive Argument Pair Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on identifying interactive argument pairs from two posts with opposite stances to a certain topic. |
Lu Ji; Zhongyu Wei; Jing Li; Qi Zhang; Xuanjing Huang; |
| 432 | On Unifying Misinformation Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. |
Nayeon Lee; Belinda Z. Li; Sinong Wang; Pascale Fung; Hao Ma; Wen-tau Yih; Madian Khabsa; |
| 433 | Frustratingly Easy Edit-based Linguistic Steganography with A Masked Language Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit edit-based linguistic steganography, with the idea that a masked language model offers an off-the-shelf solution. |
Honai Ueoka; Yugo Murawaki; Sadao Kurohashi; |
| 434 | Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper explores data augmentation-a technique particularly suitable for training with limited data-for this few-shot, highly-multiclass text classification setting. |
Jason Wei; Chengyu Huang; Soroush Vosoughi; Yu Cheng; Shiqi Xu; |
| 435 | Do RNN States Encode Abstract Phonological Alternations? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To investigate whether complex alternations are simply memorized or whether there is some level of generalization across related sound changes in a sequence-to-sequence model, we perform several experiments on Finnish consonant gradation-a complex set of sound changes triggered in some words by certain suffixes. |
Miikka Silfverberg; Francis Tyers; Garrett Nicolai; Mans Hulden; |
| 436 | Pre-training with Meta Learning for Chinese Word Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a CWS-specific pre-trained model MetaSeg, which employs a unified architecture and incorporates meta learning algorithm into a multi-criteria pre-training task. |
Zhen Ke; Liang Shi; Songtao Sun; Erli Meng; Bin Wang; Xipeng Qiu; |
| 437 | Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we tackle the task of Definition Generation (DG) in Chinese, which aims at automatically generating a definition for a word. |
Hua Zheng; Damai Dai; Lei Li; Tianyu Liu; Zhifang Sui; Baobao Chang; Yang Liu; |
| 438 | User-Generated Text Corpus for Evaluating Japanese Morphological Analysis and Lexical Normalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To evaluate and compare different MA/LN systems, we have constructed a publicly available Japanese UGT corpus. |
Shohei Higashiyama; Masao Utiyama; Taro Watanabe; Eiichiro Sumita; |
| 439 | GPT Perdetry Test: Generating New Meanings for New Words Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We create a set of nonce words and prompt GPT-3 to generate their dictionary definitions. We find GPT-3 produces plausible definitions that align with human judgments. |
Nikolay Malkin; Sameera Lanka; Pranav Goel; Sudha Rao; Nebojsa Jojic; |
| 440 | Universal Semantic Tagging for English and Mandarin Chinese Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We discuss a set of language-specific semantic phenomena, propose new annotation specifications and build a richly annotated corpus. |
Wenxi Li; Yiyang Hou; Yajie Ye; Li Liang; Weiwei Sun; |
| 441 | ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To improve the model generalization capability for rare and unseen schemas, we propose a new architecture, ShadowGNN, which processes schemas at abstract and semantic levels. |
Zhi Chen; Lu Chen; Yanbin Zhao; Ruisheng Cao; Zihan Xu; Su Zhu; Kai Yu; |
| 442 | Contextualized and Generalized Sentence Representations By Contrastive Self-Supervised Learning: A Case Study on Discourse Relation Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a method to learn contextualized and generalized sentence representations using contrastive self-supervised learning. |
Hirokazu Kiyomaru; Sadao Kurohashi; |
| 443 | AMR Parsing with Action-Pointer Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments. |
Jiawei Zhou; Tahira Naseem; Ram�n Fernandez Astudillo; Radu Florian; |
| 444 | NL-EDIT: Correcting Semantic Parse Errors Through Natural Language Interaction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. |
Ahmed Elgohary; Christopher Meek; Matthew Richardson; Adam Fourney; Gonzalo Ramos; Ahmed Hassan Awadallah; |
| 445 | Unsupervised Concept Representation Learning for Length-Varying Text Similarity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an unsupervised concept representation learning approach to address the above issues. |
Xuchao Zhang; Bo Zong; Wei Cheng; Jingchao Ni; Yanchi Liu; Haifeng Chen; |
| 446 | Augmenting Knowledge-grounded Conversations with Sequential Knowledge Transition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we proposed to explicitly model the knowledge transition in sequential multi-turn conversations by abstracting knowledge into topic tags. |
Haolan Zhan; Hainan Zhang; Hongshen Chen; Zhuoye Ding; Yongjun Bao; Yanyan Lan; |
| 447 | Adversarial Self-Supervised Learning for Out-of-Domain Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To combine the benefits of both types, we propose a self-supervised contrastive learning framework to model discriminative semantic features of both in-domain intents and OOD intents from unlabeled data. |
Zhiyuan Zeng; Keqing He; Yuanmeng Yan; Hong Xu; Weiran Xu; |
| 448 | Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a slot descriptions enhanced generative approach for zero-shot cross-domain DST. |
Zhaojiang Lin; Bing Liu; Seungwhan Moon; Paul Crook; Zhenpeng Zhou; Zhiguang Wang; Zhou Yu; Andrea Madotto; Eunjoon Cho; Rajen Subba; |
| 449 | Hierarchical Transformer for Task Oriented Dialog Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a generalized framework for Hierarchical Transformer Encoders and show how a standard transformer can be morphed into any hierarchical encoder, including HRED and HIBERT like models, by using specially designed attention masks and positional encodings. |
Bishal Santra; Potnuru Anusha; Pawan Goyal; |
| 450 | Measuring The �I Don�t Know� Problem Through The Lens of Gricean Quantity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the ‘I don’t know’ problem, in which a dialog system produces generic responses. |
Huda Khayrallah; Jo�o Sedoc; |
| 451 | RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Recursive Temporal Fact Embedding (RTFE) framework to transplant SKGE models to TKGs and to enhance the performance of existing TKGE models for TKG completion. |
Youri Xu; Haihong E; Meina Song; Wenyu Song; Xiaodong Lv; Wang Haotian; Yang Jinrui; |
| 452 | Open Hierarchical Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To establish the bidirectional connections between OpenRE and relation hierarchy, we propose the task of open hierarchical relation extraction and present a novel OHRE framework for the task. |
Kai Zhang; Yuan Yao; Ruobing Xie; Xu Han; Zhiyuan Liu; Fen Lin; Leyu Lin; Maosong Sun; |
| 453 | Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a unified framework to jointly extract explicit and implicit relational triples. |
Yubo Chen; Yunqi Zhang; Changran Hu; Yongfeng Huang; |
| 454 | Multi-Grained Knowledge Distillation for Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Drawing power from the recent advance in knowledge distillation (KD), this work presents a novel distillation scheme to efficiently transfer the knowledge learned from big models to their more affordable counterpart. |
Xuan Zhou; Xiao Zhang; Chenyang Tao; Junya Chen; Bing Xu; Wei Wang; Jing Xiao; |
| 455 | SGG: Learning to Select, Guide, and Generate for Keyphrase Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a Select-Guide-Generate (SGG) approach is proposed to deal with present and absent keyphrases generation separately with different mechanisms. |
Jing Zhao; Junwei Bao; Yifan Wang; Youzheng Wu; Xiaodong He; Bowen Zhou; |
| 456 | Towards Sentiment and Emotion Aided Multi-modal Speech Act Classification in Twitter Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a Dyadic Attention Mechanism (DAM) based multi-modal, adversarial multi-tasking framework. |
Tulika Saha; Apoorva Upadhyaya; Sriparna Saha; Pushpak Bhattacharyya; |
| 457 | Generative Imagination Elevates Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose ImagiT, a novel machine translation method via visual imagination. |
Quanyu Long; Mingxuan Wang; Lei Li; |
| 458 | Non-Autoregressive Translation By Learning Target Categorical Codes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose CNAT, which learns implicitly categorical codes as latent variables into the non-autoregressive decoding. |
Yu Bao; Shujian Huang; Tong Xiao; Dongqi Wang; Xinyu Dai; Jiajun Chen; |
| 459 | Training Data Augmentation for Code-Mixed Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an m-BERT based procedure whose core learnable component is a ternary sequence labeling model, that can be trained with a limited code-mixed corpus alone. |
Abhirut Gupta; Aditya Vavre; Sunita Sarawagi; |
| 460 | Rethinking Perturbations in Encoder-Decoders for Fast Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, this study addresses the question of whether these approaches are efficient enough for training time. |
Sho Takase; Shun Kiyono; |
| 461 | Context-aware Decoder for Neural Machine Translation Using A Target-side Document-Level Language Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We therefore present a simple method to perform context-aware decoding with any pre-trained sentence-level translation model by using a document-level language model. |
Amane Sugiyama; Naoki Yoshinaga; |
| 462 | Machine Translated Text Detection Through Text Similarity with Round-Trip Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, we propose a detector using text similarity with round-trip translation (TSRT). |
Hoang-Quoc Nguyen-Son; Tran Thao; Seira Hidano; Ishita Gupta; Shinsaku Kiyomoto; |
| 463 | TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a dynamic token reduction approach to accelerate PLMs? inference, named TR-BERT, which could flexibly adapt the layer number of each token in inference to avoid redundant calculation. |
Deming Ye; Yankai Lin; Yufei Huang; Maosong Sun; |
| 464 | Breadth First Reasoning Graph for Multi-hop Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel model of Breadth First Reasoning Graph (BFR-Graph), which presents a new message passing way that better conforms to the reasoning process. |
Yongjie Huang; Meng Yang; |
| 465 | Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering Over Knowledge Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we exploit unsupervised bilingual lexicon induction (BLI) to map training questions in source language into those in target language as augmented training data, which circumvents language inconsistency between training and inference. |
Yucheng Zhou; Xiubo Geng; Tao Shen; Wenqiang Zhang; Daxin Jiang; |
| 466 | RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose an optimized training approach, called RocketQA, to improving dense passage retrieval. |
Yingqi Qu; Yuchen Ding; Jing Liu; Kai Liu; Ruiyang Ren; Wayne Xin Zhao; Daxiang Dong; Hua Wu; Haifeng Wang; |
| 467 | DAGN: Discourse-Aware Graph Network for Logical Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore aggregating passage-level clues for solving logical reasoning QA by using discourse-based information. |
Yinya Huang; Meng Fang; Yu Cao; Liwei Wang; Xiaodan Liang; |
| 468 | Designing A Minimal Retrieve-and-Read System for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we discuss several orthogonal strategies to drastically reduce the footprint of a retrieve-and-read open-domain QA system by up to 160x. |
Sohee Yang; Minjoon Seo; |
| 469 | Unsupervised Multi-hop Question Answering By Question Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources. |
Liangming Pan; Wenhu Chen; Wenhan Xiong; Min-Yen Kan; William Yang Wang; |
| 470 | Sliding Selector Network with Dynamic Memory for Extractive Summarization of Long Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose the sliding selector network with dynamic memory for extractive summarization of long-form documents, which employs a sliding window to extract summary sentences segment by segment. |
Peng Cui; Le Hu; |
| 471 | AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a study of domain adaptation for the abstractive summarization task across six diverse target domains in a low-resource setting. |
Tiezheng Yu; Zihan Liu; Pascale Fung; |
| 472 | QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task. |
Ming Zhong; Da Yin; Tao Yu; Ahmad Zaidi; Mutethia Mutuma; Rahul Jha; Ahmed Hassan Awadallah; Asli Celikyilmaz; Yang Liu; Xipeng Qiu; Dragomir Radev; |
| 473 | MM-AVS: A Full-Scale Dataset for Multi-modal Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we release a full-scale multimodal dataset comprehensively gathering documents, summaries, images, captions, videos, audios, transcripts, and titles in English from CNN and Daily Mail. |
Xiyan Fu; Jun Wang; Zhenglu Yang; |
| 474 | MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces MediaSum, a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries. |
Chenguang Zhu; Yang Liu; Jie Mei; Michael Zeng; |
| 475 | Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the issue, we study contrast candidate generation and selection as a model-agnostic post-processing technique to correct the extrinsic hallucinations (i.e. information not present in the source text) in unfaithful summaries. |
Sihao Chen; Fan Zhang; Kazoo Sone; Dan Roth; |
| 476 | Inference Time Style Control for Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present two novel methods that can be deployed during summary decoding on any pre-trained Transformer-based summarization model. |
Shuyang Cao; Lu Wang; |