Paper Digest: ACL 2021 Highlights
Download ACL-2021-Paper-Digests.pdf– highlights of all ACL-2021 papers. 1-571 are long papers. 572-710 are short papers. Readers can also choose to read this highlight article on our console, which allows users to filter out papers using keywords, authors, etc. Annual Meeting of the Association for Computational Linguistics (ACL) is one of the top natural language processing conferences in the world. In 2021, it is to be held online.
To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper. Based in New York, Paper Digest is dedicated to producing high-quality text analysis results that people can acturally use on a daily basis. In the past 4 years, we have been serving users across the world with a number of exclusive services on ranking, search, tracking and review. This month we feature Literature Review Generator, which automatically generates literature review around any topic.
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TABLE 1: Paper Digest: ACL 2021 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Investigating Label Suggestions for Opinion Mining in German Covid-19 Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. |
Tilman Beck; Ji-Ung Lee; Christina Viehmann; Marcus Maurer; Oliver Quiring; Iryna Gurevych; |
| 2 | How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers. |
Chen Shani; Nadav Borenstein; Dafna Shahaf; |
| 3 | Engage The Public: Poll Question Generation for Social Media Posts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel task to generate poll questions for social media posts. |
Zexin Lu; Keyang Ding; Yuji Zhang; Jing Li; Baolin Peng; Lemao Liu; |
| 4 | HateCheck: Functional Tests for Hate Speech Detection Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. |
Paul R?ttger; Bertie Vidgen; Dong Nguyen; Zeerak Waseem; Helen Margetts; Janet Pierrehumbert; |
| 5 | Unified Dual-view Cognitive Model for Interpretable Claim Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) for interpretable claim verification. |
Lianwei Wu; Yuan Rao; Yuqian Lan; Ling Sun; Zhaoyin Qi; |
| 6 | DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop DeepRapper, a Transformer-based rap generation system that can model both rhymes and rhythms. |
Lanqing Xue; Kaitao Song; Duocai Wu; Xu Tan; Nevin L. Zhang; Tao Qin; Wei-Qiang Zhang; Tie-Yan Liu; |
| 7 | PENS: A Dataset and Generic Framework for Personalized News Headline Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formulate the personalized news headline generation problem whose goal is to output a user-specific title based on both a user’s reading interests and a candidate news body to be exposed to her. |
Xiang Ao; Xiting Wang; Ling Luo; Ying Qiao; Qing He; Xing Xie; |
| 8 | Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to enhance content preservation by implicitly removing the style information of each token with reverse attention, and thereby retain the content. |
Dongkyu Lee; Zhiliang Tian; Lanqing Xue; Nevin L. Zhang; |
| 9 | Mention Flags (MF): Constraining Transformer-based Text Generators Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S decoder. |
Yufei Wang; Ian Wood; Stephen Wan; Mark Dras; Mark Johnson; |
| 10 | Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the application of multilingual models in concept-to-text and propose Language Agnostic Delexicalisation, a novel delexicalisation method that uses multilingual pretrained embeddings, and employs a character-level post-editing model to inflect words in their correct form during relexicalisation. |
Giulio Zhou; Gerasimos Lampouras; |
| 11 | Conversations Are Not Flat: Modeling The Dynamic Information Flow Across Dialogue Utterances Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose the DialoFlow model, in which we introduce a dynamic flow mechanism to model the context flow, and design three training objectives to capture the information dynamics across dialogue utterances by addressing the semantic influence brought about by each utterance in large-scale pre-training. |
Zekang Li; Jinchao Zhang; Zhengcong Fei; Yang Feng; Jie Zhou; |
| 12 | Dual Slot Selector Via Local Reliability Verification for Dialogue State Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we devise the two-stage DSS-DST which consists of the Dual Slot Selector based on the current turn dialogue, and the Slot Value Generator based on the dialogue history. |
Jinyu Guo; Kai Shuang; Jijie Li; Zihan Wang; |
| 13 | Transferable Dialogue Systems and User Simulators Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to develop a modelling framework that can incorporate new dialogue scenarios through self-play between the two agents. |
Bo-Hsiang Tseng; Yinpei Dai; Florian Kreyssig; Bill Byrne; |
| 14 | BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work shows how this challenge can be addressed by disentangling persona-based dialogue generation into two sub-tasks with a novel BERT-over-BERT (BoB) model. |
Haoyu Song; Yan Wang; Kaiyan Zhang; Wei-Nan Zhang; Ting Liu; |
| 15 | GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore a non-autoregressive model for joint multiple intent detection and slot filling, achieving more fast and accurate. |
Libo Qin; Fuxuan Wei; Tianbao Xie; Xiao Xu; Wanxiang Che; Ting Liu; |
| 16 | Accelerating BERT Inference for Sequence Labeling Via Early-Exit Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The token-level early-exit brings the gap between training and inference, so we introduce an extra self-sampling fine-tuning stage to alleviate it. |
Xiaonan Li; Yunfan Shao; Tianxiang Sun; Hang Yan; Xipeng Qiu; Xuanjing Huang; |
| 17 | Modularized Interaction Network for Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Modularized Interaction Network (MIN) model which utilizes both segment-level information and word-level dependencies, and incorporates an interaction mechanism to support information sharing between boundary detection and type prediction to enhance the performance for the NER task. |
Fei Li; Zheng Wang; Siu Cheung Hui; Lejian Liao; Dandan Song; Jing Xu; Guoxiu He; Meihuizi Jia; |
| 18 | Capturing Event Argument Interaction Via A Bi-Directional Entity-Level Recurrent Decoder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the above two bottlenecks, we formalize EAE as a Seq2Seq-like learning problem for the first time, where a sentence with a specific event trigger is mapped to a sequence of event argument roles. |
Xi Xiangyu; Wei Ye; Shikun Zhang; Quanxiu Wang; Huixing Jiang; Wei Wu; |
| 19 | UniRE: A Unified Label Space for Entity Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to eliminate the different treatment on the two sub-tasks’ label spaces. |
Yijun Wang; Changzhi Sun; Yuanbin Wu; Hao Zhou; Lei Li; Junchi Yan; |
| 20 | Refining Sample Embeddings with Relation Prototypes to Enhance Continual Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fully utilize memorized samples, in this paper, we employ relation prototype to extract useful information of each relation. |
Li Cui; Deqing Yang; Jiaxin Yu; Chengwei Hu; Jiayang Cheng; Jingjie Yi; Yanghua Xiao; |
| 21 | Contrastive Learning for Many-to-many Multilingual Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to build a many-to-many translation system with an emphasis on the quality of non-English language directions. |
Xiao Pan; Mingxuan Wang; Liwei Wu; Lei Li; |
| 22 | Understanding The Properties of Minimum Bayes Risk Decoding in Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we empirically investigate the properties of MBR decoding on a number of previously reported biases and failure cases of beam search. |
Mathias M?ller; Rico Sennrich; |
| 23 | Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To enable sequence-level parallelization of LSTMs, we approximate full LSTM context modelling by computing hidden states and gates with the current input and a simple bag-of-words representation of the preceding tokens context. |
Hongfei Xu; Qiuhui Liu; Josef van Genabith; Deyi Xiong; Meng Zhang; |
| 24 | A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a bidirectional Transformer based alignment (BTBA) model for unsupervised learning of the word alignment task. |
Jingyi Zhang; Josef van Genabith; |
| 25 | Learning Language Specific Sub-network for Multilingual Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose LaSS to jointly train a single unified multilingual MT model. |
Zehui Lin; Liwei Wu; Mingxuan Wang; Lei Li; |
| 26 | Exploring The Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an alternative by describing and evaluating an approach to automatically generating counterfactual data for the purpose of data augmentation and explanation. |
Linyi Yang; Jiazheng Li; Padraig Cunningham; Yue Zhang; Barry Smyth; Ruihai Dong; |
| 27 | Bridge-Based Active Domain Adaptation for Aspect Term Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel active domain adaptation method. |
Zhuang Chen; Tieyun Qian; |
| 28 | Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Multi-channel Graph Neural Networks with Sentiment-awareness (MGNNS) for image-text sentiment detection. |
Xiaocui Yang; Shi Feng; Yifei Zhang; Daling Wang; |
| 29 | Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a new task, named Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction, with the goal to extract all aspect-category-opinion-sentiment quadruples in a review sentence and provide full support for aspect-based sentiment analysis with implicit aspects and opinions. |
Hongjie Cai; Rui Xia; Jianfei Yu; |
| 30 | PASS: Perturb-and-Select Summarizer for Product Reviews Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the PASS system (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based model (T5 in our case), which follows a few-shot fine-tuning scheme. |
Nadav Oved; Ran Levy; |
| 31 | Deep Differential Amplifier for Extractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we conceptualize the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework. |
Ruipeng Jia; Yanan Cao; Fang Fang; Yuchen Zhou; Zheng Fang; Yanbing Liu; Shi Wang; |
| 32 | Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization By Generating Multiple Summaries Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). |
Yi Yu; Adam Jatowt; Antoine Doucet; Kazunari Sugiyama; Masatoshi Yoshikawa; |
| 33 | Self-Supervised Multimodal Opinion Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To use the abundant information contained in non-text data, we propose a self-supervised multimodal opinion summarization framework called MultimodalSum. |
Jinbae Im; Moonki Kim; Hoyeop Lee; Hyunsouk Cho; Sehee Chung; |
| 34 | A Training-free and Reference-free Summarization Evaluation Metric Via Centrality-weighted Relevance and Self-referenced Redundancy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To avoid these limitations, we propose a training-free and reference-free summarization evaluation metric. |
Wang Chen; Piji Li; Irwin King; |
| 35 | DESCGEN: A Distantly Supervised Datasetfor Generating Entity Descriptions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce DESCGEN: given mentions spread over multiple documents, the goal is to generate an entity summary description. |
Weijia Shi; Mandar Joshi; Luke Zettlemoyer; |
| 36 | Introducing Orthogonal Constraint in Structural Probes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a new type of structural probing, where the linear projection is decomposed into 1. iso-morphic space rotation; 2. linear scaling that identifies and scales the most relevant dimensions. |
Tomasz Limisiewicz; David Marecek; |
| 37 | Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to use the syntactic structure as the trigger in textual backdoor attacks. |
Fanchao Qi; Mukai Li; Yangyi Chen; Zhengyan Zhang; Zhiyuan Liu; Yasheng Wang; Maosong Sun; |
| 38 | Examining The Inductive Bias of Neural Language Models with Artificial Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel method for investigating the inductive biases of language models using artificial languages. |
Jennifer C. White; Ryan Cotterell; |
| 39 | Explaining Contextualization in Language Models Using Visual Analytics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we contribute to the current efforts of explaining such models by exploring the continuum between function and content words with respect to contextualization in BERT, based on linguistically-informed insights. |
Rita Sevastjanova; Aikaterini-Lida Kalouli; Christin Beck; Hanna Sch?fer; Mennatallah El-Assady; |
| 40 | Improving The Faithfulness of Attention-based Explanations with Task-specific Information for Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we seek to improve the faithfulness of attention-based explanations for text classification. |
George Chrysostomou; Nikolaos Aletras; |
| 41 | Generating Landmark Navigation Instructions from Maps As A Graph-to-Text Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a neural model that takes OpenStreetMap representations as input and learns to generate navigation instructions that contain visible and salient landmarks from human natural language instructions. |
Raphael Schumann; Stefan Riezler; |
| 42 | E2E-VLP: End-to-End Vision-Language Pre-training Enhanced By Visual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the first end-to-end vision-language pre-trained model for both V+L understanding and generation, namely E2E-VLP, where we build a unified Transformer framework to jointly learn visual representation, and semantic alignments between image and text. |
Haiyang Xu; Ming Yan; Chenliang Li; Bin Bi; Songfang Huang; Wenming Xiao; Fei Huang; |
| 43 | Learning Relation Alignment for Calibrated Cross-modal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In response, we present Inter-modal Alignment on Intra-modal Self-attentions (IAIS), a regularized training method to optimize the ISD and calibrate intra-modal self-attentions from the two modalities mutually via inter-modal alignment. |
Shuhuai Ren; Junyang Lin; Guangxiang Zhao; Rui Men; An Yang; Jingren Zhou; Xu Sun; Hongxia Yang; |
| 44 | KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformer-based sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts. |
Yiran Xing; Zai Shi; Zhao Meng; Gerhard Lakemeyer; Yunpu Ma; Roger Wattenhofer; |
| 45 | Cascaded Head-colliding Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present cascaded head-colliding attention (CODA) which explicitly models the interactions between attention heads through a hierarchical variational distribution. |
Lin Zheng; Zhiyong Wu; Lingpeng Kong; |
| 46 | Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we derive a factorized form of the knowledge distillation objective for structured prediction, which is tractable for many typical choices of the teacher and student models. |
Xinyu Wang; Yong Jiang; Zhaohui Yan; Zixia Jia; Nguyen Bach; Tao Wang; Zhongqiang Huang; Fei Huang; Kewei Tu; |
| 47 | Parameter-efficient Multi-task Fine-tuning for Transformers Via Shared Hypernetworks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show that we can learn adapter parameters for all layers and tasks by generating them using shared hypernetworks, which condition on task, adapter position, and layer id in a transformer model. |
Rabeeh Karimi Mahabadi; Sebastian Ruder; Mostafa Dehghani; James Henderson; |
| 48 | COSY: COunterfactual SYntax for Cross-Lingual Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We tackle this issue by incorporating language-agnostic information, specifically, universal syntax such as dependency relations and POS tags, into language models, based on the observation that universal syntax is transferable across different languages. |
Sicheng Yu; Hao Zhang; Yulei Niu; Qianru Sun; Jing Jiang; |
| 49 | OoMMix: Out-of-manifold Regularization in Contextual Embedding Space for Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new approach, called OoMMix, to finding and regularizing the remainder of the space, referred to as out-of-manifold, which cannot be accessed through the words. |
Seonghyeon Lee; Dongha Lee; Hwanjo Yu; |
| 50 | Understanding and Countering Stereotypes: A Computational Approach to The Stereotype Content Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology. |
Kathleen C. Fraser; Isar Nejadgholi; Svetlana Kiritchenko; |
| 51 | Structurizing Misinformation Stories Via Rationalizing Fact-Checks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper aims to structurize these misinformation stories by leveraging fact-check articles. |
Shan Jiang; Christo Wilson; |
| 52 | Modeling Language Usage and Listener Engagement in Podcasts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate how various factors – vocabulary diversity, distinctiveness, emotion, and syntax, among others – correlate with engagement, based on analysis of the creators’ written descriptions and transcripts of the audio. |
Sravana Reddy; Mariya Lazarova; Yongze Yu; Rosie Jones; |
| 53 | Breaking Down The Invisible Wall of Informal Fallacies in Online Discussions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the most frequent fallacies on Reddit, and we present them using the pragma-dialectical theory of argumentation. |
Saumya Sahai; Oana Balalau; Roxana Horincar; |
| 54 | SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. |
Liang Qiu; Yuan Liang; Yizhou Zhao; Pan Lu; Baolin Peng; Zhou Yu; Ying Nian Wu; Song-Chun Zhu; |
| 55 | TicketTalk: Toward Human-level Performance with End-to-end, Transaction-based Dialog Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a data-driven, end-to-end approach to transaction-based dialog systems that performs at near-human levels in terms of verbal response quality and factual grounding accuracy. |
Bill Byrne; Karthik Krishnamoorthi; Saravanan Ganesh; Mihir Kale; |
| 56 | Improving Dialog Systems for Negotiation with Personality Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent?s high-level strategy in negotiation tasks. |
Runzhe Yang; Jingxiao Chen; Karthik Narasimhan; |
| 57 | Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. |
Wangchunshu Zhou; Qifei Li; Chenle Li; |
| 58 | Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose different evaluation measures to disentangle these different styles of responses by quantifying the informativeness and objectivity. |
Hannah Rashkin; David Reitter; Gaurav Singh Tomar; Dipanjan Das; |
| 59 | CitationIE: Leveraging The Citation Graph for Scientific Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In contrast to prior work, we augment our text representations by leveraging a complementary source of document context: the citation graph of referential links between citing and cited papers. |
Vijay Viswanathan; Graham Neubig; Pengfei Liu; |
| 60 | From Discourse to Narrative: Knowledge Projection for Event Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a knowledge projection paradigm for event relation extraction: projecting discourse knowledge to narratives by exploiting the commonalities between them. |
Jialong Tang; Hongyu Lin; Meng Liao; Yaojie Lu; Xianpei Han; Le Sun; Weijian Xie; Jin Xu; |
| 61 | AdvPicker: Effectively Leveraging Unlabeled Data Via Adversarial Discriminator for Cross-Lingual NER Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We design an adversarial learning framework in which an encoder learns entity domain knowledge from labeled source-language data and better shared features are captured via adversarial training – where a discriminator selects less language-dependent target-language data via similarity to the source language. |
Weile Chen; Huiqiang Jiang; Qianhui Wu; B?rje Karlsson; Yi Guan; |
| 62 | Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel end-to-end graph neural model called CompareNet, which compares the news to the knowledge base (KB) through entities for fake news detection. |
Linmei Hu; Tianchi Yang; Luhao Zhang; Wanjun Zhong; Duyu Tang; Chuan Shi; Nan Duan; Ming Zhou; |
| 63 | Discontinuous Named Entity Recognition As Maximal Clique Discovery Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we first construct a segment graph for each sentence, in which each node denotes a segment (a continuous entity on its own, or a part of discontinuous entities), and an edge links two nodes that belong to the same entity. |
Yucheng Wang; Bowen Yu; Hongsong Zhu; Tingwen Liu; Nan Yu; Limin Sun; |
| 64 | LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we take a different, neuro-symbolic approach that combines the advantages of using interpretable rules based on first-order logic with the performance of neural learning. |
Hang Jiang; Sairam Gurajada; Qiuhao Lu; Sumit Neelam; Lucian Popa; Prithviraj Sen; Yunyao Li; Alexander Gray; |
| 65 | Do Context-Aware Translation Models Pay The Right Attention? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To answer these questions, we introduce SCAT (Supporting Context for Ambiguous Translations), a new English-French dataset comprising supporting context words for 14K translations that professional translators found useful for pronoun disambiguation. |
Kayo Yin; Patrick Fernandes; Danish Pruthi; Aditi Chaudhary; Andr? F. T. Martins; Graham Neubig; |
| 66 | Adapting High-resource NMT Models to Translate Low-resource Related Languages Without Parallel Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we exploit this linguistic overlap to facilitate translating to and from a low-resource language with only monolingual data, in addition to any parallel data in the related high-resource language. |
Wei-Jen Ko; Ahmed El-Kishky; Adithya Renduchintala; Vishrav Chaudhary; Naman Goyal; Francisco Guzm?n; Pascale Fung; Philipp Koehn; Mona Diab; |
| 67 | Bilingual Lexicon Induction Via Unsupervised Bitext Construction and Word Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show it is possible to produce much higher quality lexicons with methods that combine (1) unsupervised bitext mining and (2) unsupervised word alignment. |
Haoyue Shi; Luke Zettlemoyer; Sida I. Wang; |
| 68 | Multilingual Speech Translation from Efficient Finetuning of Pretrained Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a simple yet effective approach to build multilingual speech-to-text (ST) translation through efficient transfer learning from a pretrained speech encoder and text decoder. |
Xian Li; Changhan Wang; Yun Tang; Chau Tran; Yuqing Tang; Juan Pino; Alexei Baevski; Alexis Conneau; Michael Auli; |
| 69 | Learning Faithful Representations of Causal Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we define the faithfulness property of contextual embeddings to capture geometric distance-based properties of directed acyclic causal graphs. |
Ananth Balashankar; Lakshminarayanan Subramanian; |
| 70 | What Context Features Can Transformer Language Models Use? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe a series of experiments that measure usable information by selectively ablating lexical and structural information in transformer language models trained on English Wikipedia. |
Joe O?Connor; Jacob Andreas; |
| 71 | Integrated Directional Gradients: Feature Interaction Attribution for Neural NLP Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce Integrated Directional Gradients (IDG), a method for attributing importance scores to groups of features, indicating their relevance to the output of a neural network model for a given input. |
Sandipan Sikdar; Parantapa Bhattacharya; Kieran Heese; |
| 72 | DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. |
John Giorgi; Osvald Nitski; Bo Wang; Gary Bader; |
| 73 | XLPT-AMR: Cross-Lingual Pre-Training Via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Upon the availability of English AMR dataset and English-to- X parallel datasets, in this paper we propose a novel cross-lingual pre-training approach via multi-task learning (MTL) for both zeroshot AMR parsing and AMR-to-text generation. |
Dongqin Xu; Junhui Li; Muhua Zhu; Min Zhang; Guodong Zhou; |
| 74 | Span-based Semantic Parsing for Compositional Generalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we posit that a span-based parser should lead to better compositional generalization. |
Jonathan Herzig; Jonathan Berant; |
| 75 | Compositional Generalization and Natural Language Variation: Can A Semantic Parsing Approach Handle Both? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we ask: can we develop a semantic parsing approach that handles both natural language variation and compositional generalization? |
Peter Shaw; Ming-Wei Chang; Panupong Pasupat; Kristina Toutanova; |
| 76 | A Targeted Assessment of Incremental Processing in Neural Language Models and Humans Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a targeted, scaled-up comparison of incremental processing in humans and neural language models by collecting by-word reaction time data for sixteen different syntactic test suites across a range of structural phenomena. |
Ethan Wilcox; Pranali Vani; Roger Levy; |
| 77 | The Possible, The Plausible, and The Desirable: Event-Based Modality Detection for Language Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work builds on the theoretical foundations of the Georgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to propose an event-based modality detection task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. |
Valentina Pyatkin; Shoval Sadde; Aynat Rubinstein; Paul Portner; Reut Tsarfaty; |
| 78 | To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes an empirical study about the effect that POS tags have on two computational morphological tasks with the Transformer architecture. |
Sarah Moeller; Ling Liu; Mans Hulden; |
| 79 | Prosodic Segmentation for Parsing Spoken Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate how prosody affects a parser that receives an entire dialogue turn as input (a turn-based model), instead of gold standard pre-segmented SUs (an SU-based model). |
Elizabeth Nielsen; Mark Steedman; Sharon Goldwater; |
| 80 | VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce VoxPopuli, a large-scale multilingual corpus providing 400K hours of unlabeled speech data in 23 languages. |
Changhan Wang; Morgane Riviere; Ann Lee; Anne Wu; Chaitanya Talnikar; Daniel Haziza; Mary Williamson; Juan Pino; Emmanuel Dupoux; |
| 81 | Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We examine four such benchmarks constructed for two NLP tasks: language modeling and coreference resolution. |
Su Lin Blodgett; Gilsinia Lopez; Alexandra Olteanu; Robert Sim; Hanna Wallach; |
| 82 | Robust Knowledge Graph Completion with Stacked Convolutions and A Student Re-Ranking Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop a deep convolutional network that utilizes textual entity representations and demonstrate that our model outperforms recent KG completion methods in this challenging setting. |
Justin Lovelace; Denis Newman-Griffis; Shikhar Vashishth; Jill Fain Lehman; Carolyn Ros?; |
| 83 | A DQN-based Approach to Finding Precise Evidences for Fact Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the strong exploration ability of the deep Q-learning network (DQN), we propose a DQN-based approach to retrieval of precise evidences. |
Hai Wan; Haicheng Chen; Jianfeng Du; Weilin Luo; Rongzhen Ye; |
| 84 | The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this void in the literature, we study in this paper selective prediction for NLP, comparing different models and confidence estimators. |
Ji Xin; Raphael Tang; Yaoliang Yu; Jimmy Lin; |
| 85 | Unsupervised Out-of-Domain Detection Via Pre-trained Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our work tackles the problem of detecting out-of-domain samples with only unsupervised in-domain data. |
Keyang Xu; Tongzheng Ren; Shikun Zhang; Yihao Feng; Caiming Xiong; |
| 86 | MATE-KD: Masked Adversarial TExt, A Companion to Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present MATE-KD, a novel text-based adversarial training algorithm which improves the performance of knowledge distillation. |
Ahmad Rashid; Vasileios Lioutas; Mehdi Rezagholizadeh; |
| 87 | Selecting Informative Contexts Improves Language Model Fine-tuning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning. |
Richard Antonello; Nicole Beckage; Javier Turek; Alexander Huth; |
| 88 | Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show that text simplification can be decomposed into a compact pipeline of tasks to ensure the transparency and explainability of the process. |
Cristina Garbacea; Mengtian Guo; Samuel Carton; Qiaozhu Mei; |
| 89 | Multi-Task Retrieval for Knowledge-Intensive Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Driven by the question of whether a neural retrieval model can be _universal_ and perform robustly on a wide variety of problems, we propose a multi-task trained model. |
Jean Maillard; Vladimir Karpukhin; Fabio Petroni; Wen-tau Yih; Barlas Oguz; Veselin Stoyanov; Gargi Ghosh; |
| 90 | When Do You Need Billions of Words of Pretraining Data? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To explore this question, we adopt five styles of evaluation: classifier probing, information-theoretic probing, unsupervised relative acceptability judgments, unsupervised language model knowledge probing, and fine-tuning on NLU tasks. |
Yian Zhang; Alex Warstadt; Xiaocheng Li; Samuel R. Bowman; |
| 91 | Analyzing The Source and Target Contributions to Predictions in Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We argue that this relative contribution can be evaluated by adopting a variant of Layerwise Relevance Propagation (LRP). |
Elena Voita; Rico Sennrich; Ivan Titov; |
| 92 | Comparing Test Sets with Item Response Theory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To measure this uniformly across datasets, we draw on Item Response Theory and evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. |
Clara Vania; Phu Mon Htut; William Huang; Dhara Mungra; Richard Yuanzhe Pang; Jason Phang; Haokun Liu; Kyunghyun Cho; Samuel R. Bowman; |
| 93 | Uncovering Constraint-Based Behavior in Neural Models Via Targeted Fine-Tuning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our results suggest that models need to learn both the linguistic constraints in a language and their relative ranking, with mismatches in either producing non-human-like behavior. |
Forrest Davis; Marten van Schijndel; |
| 94 | More Identifiable Yet Equally Performant Transformers for Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we provide deeper theoretical analysis and empirical observations on the identifiability of attention weights. |
Rishabh Bhardwaj; Navonil Majumder; Soujanya Poria; Eduard Hovy; |
| 95 | AugNLG: Few-shot Natural Language Generation Using Self-trained Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes AugNLG, a novel data augmentation approach that combines a self-trained neural retrieval model with a few-shot learned NLU model, to automatically create MR-to-Text data from open-domain texts. |
Xinnuo Xu; Guoyin Wang; Young-Bum Kim; Sungjin Lee; |
| 96 | Can Vectors Read Minds Better Than Experts’ Comparing Data Augmentation Strategies for The Automated Scoring of Children’s Mindreading Ability Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children’s ability to understand others’ thoughts, feelings, and desires (or mindreading). |
Venelin Kovatchev; Phillip Smith; Mark Lee; Rory Devine; |
| 97 | A Dataset and Baselines for Multilingual Reply Suggestion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead, we present MRS, a multilingual reply suggestion dataset with ten languages. |
Mozhi Zhang; Wei Wang; Budhaditya Deb; Guoqing Zheng; Milad Shokouhi; Ahmed Hassan Awadallah; |
| 98 | What Ingredients Make for An Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we compare the efficacy of interventions that have been proposed in prior work as ways of improving data quality. |
Nikita Nangia; Saku Sugawara; Harsh Trivedi; Alex Warstadt; Clara Vania; Samuel R. Bowman; |
| 99 | Align Voting Behavior with Public Statements for Legislator Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to mitigate these two problems, we explore to incorporate both voting behavior and public statements on Twitter to jointly model legislators. |
Xinyi Mou; Zhongyu Wei; Lei Chen; Shangyi Ning; Yancheng He; Changjian Jiang; Xuanjing Huang; |
| 100 | Measure and Evaluation of Semantic Divergence Across Two Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to track these divergences by comparing the evolution of a word and its translation across two languages. |
Syrielle Montariol; Alexandre Allauzen; |
| 101 | Improving Zero-Shot Translation By Disentangling Positional Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We demonstrate that a main factor causing the language-specific representations is the positional correspondence to input tokens. We show that this can be easily alleviated by removing residual connections in an encoder layer. |
Danni Liu; Jan Niehues; James Cross; Francisco Guzm?n; Xian Li; |
| 102 | Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning (CSR) beyond English. |
Bill Yuchen Lin; Seyeon Lee; Xiaoyang Qiao; Xiang Ren; |
| 103 | Attention Calibration for Transformer in Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to calibrate the attention weights by introducing a mask perturbation model that automatically evaluates each input’s contribution to the model outputs. |
Yu Lu; Jiali Zeng; Jiajun Zhang; Shuangzhi Wu; Mu Li; |
| 104 | Diverse Pretrained Context Encodings Improve Document Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new architecture for adapting a sentence-level sequence-to-sequence transformer by incorporating multiple pre-trained document context signals and assess the impact on translation performance of (1) different pretraining approaches for generating these signals, (2) the quantity of parallel data for which document context is available, and (3) conditioning on source, target, or source and target contexts. |
Domenic Donato; Lei Yu; Chris Dyer; |
| 105 | Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we argue that relatedness among languages in a language family may be exploited to overcome some of the corpora limitations of LRLs, and propose RelateLM. |
Yash Khemchandani; Sarvesh Mehtani; Vaidehi Patil; Abhijeet Awasthi; Partha Talukdar; Sunita Sarawagi; |
| 106 | On Finding The K-best Non-projective Dependency Trees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a simplification of the K-best spanning tree algorithm of Camerini et al. (1980). |
Ran Zmigrod; Tim Vieira; Ryan Cotterell; |
| 107 | Towards Argument Mining for Social Good: A Survey Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel definition of argument quality which is integrated with that of deliberative quality from the Social Science literature. |
Eva Maria Vecchi; Neele Falk; Iman Jundi; Gabriella Lapesa; |
| 108 | Automated Generation of Storytelling Vocabulary from Photographs for Use in AAC Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We contribute a novel method for generating context-related vocabulary from photographs of personally relevant events aimed at supporting people with language impairments in retelling their past experiences. |
Mauricio Fontana de Vargas; Karyn Moffatt; |
| 109 | CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we describe our creation of a dataset of clinical action items annotated over MIMIC-III, the largest publicly available dataset of real clinical notes. |
James Mullenbach; Yada Pruksachatkun; Sean Adler; Jennifer Seale; Jordan Swartz; Greg McKelvey; Hui Dai; Yi Yang; David Sontag; |
| 110 | Assessing Emoji Use in Modern Text Processing Tools Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we assess this support by considering test sets of tweets with emojis, based on which we perform a series of experiments investigating the ability of prominent NLP and text processing tools to adequately process them. |
Abu Awal Md Shoeb; Gerard de Melo; |
| 111 | Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this limitation, we propose SEG-Net, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences. |
Wasi Ahmad; Xiao Bai; Soomin Lee; Kai-Wei Chang; |
| 112 | Factorising Meaning and Form for Intent-Preserving Paraphrasing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a method for generating paraphrases of English questions that retain the original intent but use a different surface form. |
Tom Hosking; Mirella Lapata; |
| 113 | AggGen: Ordering and Aggregating While Generating Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present AggGen (pronounced ‘again’) a data-to-text model which re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input aggregation. |
Xinnuo Xu; Ondrej Du?ek; Verena Rieser; Ioannis Konstas; |
| 114 | Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present Reflective Decoding, a novel unsupervised algorithm that allows for direct application of unidirectional LMs to non-sequential tasks. |
Peter West; Ximing Lu; Ari Holtzman; Chandra Bhagavatula; Jena D. Hwang; Yejin Choi; |
| 115 | Towards Table-to-Text Generation with Numerical Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a framework consisting of a pre-trained model and a copy mechanism. |
Lya Hulliyyatus Suadaa; Hidetaka Kamigaito; Kotaro Funakoshi; Manabu Okumura; Hiroya Takamura; |
| 116 | BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper. |
Yubin Ge; Ly Dinh; Xiaofeng Liu; Jinsong Su; Ziyao Lu; Ante Wang; Jana Diesner; |
| 117 | Language Model As An Annotator: Exploring DialoGPT for Dialogue Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show how DialoGPT, a pre-trained model for conversational response generation, can be developed as an unsupervised dialogue annotator, which takes advantage of dialogue background knowledge encoded in DialoGPT. |
Xiachong Feng; Xiaocheng Feng; Libo Qin; Bing Qin; Ting Liu; |
| 118 | Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our controlled experiments suggest two headrooms – paragraph selection and answerability prediction, i.e. whether the paired evidence document contains the answer to the query or not. |
Akari Asai; Eunsol Choi; |
| 119 | A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this problem, we propose a novel Multi-Task Learning (MTL) method with data augmentation for medical question understanding. |
Khalil Mrini; Franck Dernoncourt; Seunghyun Yoon; Trung Bui; Walter Chang; Emilia Farcas; Ndapa Nakashole; |
| 120 | Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents the first approach for zero-shot NERC, introducing novel architectures that leverage the fact that textual descriptions for many entity classes occur naturally. |
Rami Aly; Andreas Vlachos; Ryan McDonald; |
| 121 | MECT: Multi-Metadata Embedding Based Cross-Transformer for Chinese Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel Multi-metadata Embedding based Cross-Transformer (MECT) to improve the performance of Chinese NER by fusing the structural information of Chinese characters. |
Shuang Wu; Xiaoning Song; Zhenhua Feng; |
| 122 | Factuality Assessment As Modal Dependency Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we frame factuality assessment as a modal dependency parsing task that identifies the events and their sources, formally known as conceivers, and then determine the level of certainty that the sources are asserting with respect to the events. |
Jiarui Yao; Haoling Qiu; Jin Zhao; Bonan Min; Nianwen Xue; |
| 123 | Directed Acyclic Graph Network for Conversational Emotion Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. |
Weizhou Shen; Siyue Wu; Yunyi Yang; Xiaojun Quan; |
| 124 | Improving Formality Style Transfer with Context-Aware Rule Injection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we present Context-Aware Rule Injection (CARI), an innovative method for formality style transfer (FST) by injecting multiple rules into an end-to-end BERT-based encoder and decoder model. |
Zonghai Yao; Hong Yu; |
| 125 | Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. |
Lixing Zhu; Gabriele Pergola; Lin Gui; Deyu Zhou; Yulan He; |
| 126 | Syntopical Graphs for Computational Argumentation Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a general approach to these tasks motivated by syntopical reading, a reading process that emphasizes comparing and contrasting viewpoints in order to improve topic understanding. |
Joe Barrow; Rajiv Jain; Nedim Lipka; Franck Dernoncourt; Vlad Morariu; Varun Manjunatha; Douglas Oard; Philip Resnik; Henning Wachsmuth; |
| 127 | Stance Detection in COVID-19 Tweets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We annotate a new stance detection dataset, called COVID-19-Stance. |
Kyle Glandt; Sarthak Khanal; Yingjie Li; Doina Caragea; Cornelia Caragea; |
| 128 | Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence; 2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring semantic similarity between the global topic information and the semantic representation of evidence; 4) aggregating evidence based on their implicit stances to the claim. |
Jiasheng Si; Deyu Zhou; Tongzhe Li; Xingyu Shi; Yulan He; |
| 129 | Changes in European Solidarity Before and During COVID-19: Evidence from A Large Crowd- and Expert-Annotated Twitter Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the well-established social scientific concept of social solidarity and its contestation, anti-solidarity, as a new problem setting to supervised machine learning in NLP to assess how European solidarity discourses changed before and after the COVID-19 outbreak was declared a global pandemic. |
Alexandra Ils; Dan Liu; Daniela Grunow; Steffen Eger; |
| 130 | Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a framework for computationally measuring uptake, by (1) releasing a dataset of student-teacher exchanges extracted from US math classroom transcripts annotated for uptake by experts; (2) formalizing uptake as pointwise Jensen-Shannon Divergence (pJSD), estimated via next utterance classification; (3) conducting a linguistically-motivated comparison of different unsupervised measures and (4) correlating these measures with educational outcomes. |
Dorottya Demszky; Jing Liu; Zid Mancenido; Julie Cohen; Heather Hill; Dan Jurafsky; Tatsunori Hashimoto; |
| 131 | A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this gap, we offer a survey of code-switching (C-S) covering the literature in linguistics with a reflection on the key issues in language technologies. |
A. Seza Dogru?z; Sunayana Sitaram; Barbara E. Bullock; Almeida Jacqueline Toribio; |
| 132 | Learning from The Worst: Dynamically Generated Datasets to Improve Online Hate Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a human-and-model-in-the-loop process for dynamically generating datasets and training better performing and more robust hate detection models. |
Bertie Vidgen; Tristan Thrush; Zeerak Waseem; Douwe Kiela; |
| 133 | InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We contribute a novel benchmark for fake news detection at the knowledge element level, as well as a solution for this task which incorporates cross-media consistency checking to detect the fine-grained knowledge elements making news articles misinformative. |
Yi Fung; Christopher Thomas; Revanth Gangi Reddy; Sandeep Polisetty; Heng Ji; Shih-Fu Chang; Kathleen McKeown; Mohit Bansal; Avi Sil; |
| 134 | I Like Fish, Especially Dolphins: Addressing Contradictions in Dialogue Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To quantify how well natural language understanding models can capture consistency in a general conversation, we introduce the DialoguE COntradiction DEtection task (DECODE) and a new conversational dataset containing both human-human and human-bot contradictory dialogues. |
Yixin Nie; Mary Williamson; Mohit Bansal; Douwe Kiela; Jason Weston; |
| 135 | A Sequence-to-Sequence Approach to Dialogue State Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new approach to dialogue state tracking, referred to as Seq2Seq-DU, which formalizes DST as a sequence-to-sequence problem. |
Yue Feng; Yang Wang; Hang Li; |
| 136 | Discovering Dialog Structure Graph for Coherent Dialog Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we present an unsupervised model, Discrete Variational Auto-Encoder with Graph Neural Network (DVAE-GNN), to discover discrete hierarchical latent dialog states (at the level of both session and utterance) and their transitions from corpus as a dialog structure graph. |
Jun Xu; Zeyang Lei; Haifeng Wang; Zheng-Yu Niu; Hua Wu; Wanxiang Che; |
| 137 | Dialogue Response Selection with Hierarchical Curriculum Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an easy-to-difficult scheme. |
Yixuan Su; Deng Cai; Qingyu Zhou; Zibo Lin; Simon Baker; Yunbo Cao; Shuming Shi; Nigel Collier; Yan Wang; |
| 138 | A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. |
Jingxuan Yang; Kerui Xu; Jun Xu; Si Li; Sheng Gao; Jun Guo; Nianwen Xue; Ji-Rong Wen; |
| 139 | A Systematic Investigation of KB-Text Embedding Alignment at Scale Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We conduct a large-scale, systematic investigation of aligning KB and text embeddings for joint reasoning. |
Vardaan Pahuja; Yu Gu; Wenhu Chen; Mehdi Bahrami; Lei Liu; Wei-Peng Chen; Yu Su; |
| 140 | Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a new multi-stage computational framework – NEEDLE with three essential ingredients: (1) weak label completion, (2) noise-aware loss function, and (3) final fine-tuning over the strongly labeled data. |
Haoming Jiang; Danqing Zhang; Tianyu Cao; Bing Yin; Tuo Zhao; |
| 141 | Ultra-Fine Entity Typing with Weak Supervision from A Masked Language Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To remedy this problem, in this paper, we propose to obtain training data for ultra-fine entity typing by using a BERT Masked Language Model (MLM). |
Hongliang Dai; Yangqiu Song; Haixun Wang; |
| 142 | Improving Named Entity Recognition By External Context Retrieving and Cooperative Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine, with the original sentence as the query. |
Xinyu Wang; Yong Jiang; Nguyen Bach; Tao Wang; Zhongqiang Huang; Fei Huang; Kewei Tu; |
| 143 | Implicit Representations of Meaning in Neural Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In BART and T5 transformer language models, we identify contextual word representations that function as *models of entities and situations* as they evolve throughout a discourse. |
Belinda Z. Li; Maxwell Nye; Jacob Andreas; |
| 144 | Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To elucidate the mechanisms by which the models accomplish this behavior, this study applies causal mediation analysis to pre-trained neural language models. |
Matthew Finlayson; Aaron Mueller; Sebastian Gehrmann; Stuart Shieber; Tal Linzen; Yonatan Belinkov; |
| 145 | Bird’s Eye: Probing for Linguistic Graph Structures with A Simple Information-Theoretic Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new information-theoretic probe, Bird’s Eye, which is a fairly simple probe method for detecting if and how these representations encode the information in these linguistic graphs. |
Yifan Hou; Mrinmaya Sachan; |
| 146 | Knowledgeable or Educated Guess? Revisiting Language Models As Knowledge Bases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we conduct a rigorous study to explore the underlying predicting mechanisms of MLMs over different extraction paradigms. |
Boxi Cao; Hongyu Lin; Xianpei Han; Le Sun; Lingyong Yan; Meng Liao; Tong Xue; Jin Xu; |
| 147 | Poisoning Knowledge Graph Embeddings Via Relation Inference Patterns Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To poison KGE models, we propose to exploit their inductive abilities which are captured through the relationship patterns like symmetry, inversion and composition in the knowledge graph. |
Peru Bhardwaj; John Kelleher; Luca Costabello; Declan O?Sullivan; |
| 148 | Bad Seeds: Evaluating Lexical Methods for Bias Measurement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We gather seeds used in prior work, documenting their common sources and rationales, and in case studies of three English-language corpora, we enumerate the different types of social biases and linguistic features that, once encoded in the seeds, can affect subsequent bias measurements. |
Maria Antoniak; David Mimno; |
| 149 | A Survey of Race, Racism, and Anti-Racism in NLP Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we survey 79 papers from the ACL anthology that mention race. |
Anjalie Field; Su Lin Blodgett; Zeerak Waseem; Yulia Tsvetkov; |
| 150 | Intrinsic Bias Metrics Do Not Correlate with Application Bias Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We compare intrinsic and extrinsic metrics across hundreds of trained models covering different tasks and experimental conditions. |
Seraphina Goldfarb-Tarrant; Rebecca Marchant; Ricardo Mu?oz S?nchez; Mugdha Pandya; Adam Lopez; |
| 151 | RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present REDDITBIAS, the first conversational data set grounded in the actual human conversations from Reddit, allowing for bias measurement and mitigation across four important bias dimensions: gender,race,religion, and queerness. |
Soumya Barikeri; Anne Lauscher; Ivan Vulic; Goran Glava?; |
| 152 | Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper studies the relative importance of attention heads in Transformer-based models to aid their interpretability in cross-lingual and multi-lingual tasks. |
Weicheng Ma; Kai Zhang; Renze Lou; Lili Wang; Soroush Vosoughi; |
| 153 | Crafting Adversarial Examples for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate veritable evaluations of NMT adversarial attacks, and propose a novel method to craft NMT adversarial examples. |
Xinze Zhang; Junzhe Zhang; Zhenhua Chen; Kun He; |
| 154 | UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose UXLA, a novel unsupervised data augmentation framework for zero-resource transfer learning scenarios. |
M Saiful Bari; Tasnim Mohiuddin; Shafiq Joty; |
| 155 | Glancing Transformer for Non-Autoregressive Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the Glancing Language Model (GLM) for single-pass parallel generation models. |
Lihua Qian; Hao Zhou; Yu Bao; Mingxuan Wang; Lin Qiu; Weinan Zhang; Yong Yu; Lei Li; |
| 156 | Hierarchical Context-aware Network for Dense Video Event Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a novel Hierarchical Context-aware Network for dense video event captioning (HCN) to capture context from various aspects. |
Lei Ji; Xianglin Guo; Haoyang Huang; Xilin Chen; |
| 157 | Control Image Captioning Spatially and Temporally Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper aims to solve this problem by proposing a novel model called LoopCAG, which connects Contrastive constraints and Attention Guidance in a Loop manner, engaged explicit spatial and temporal constraints to the generating process. |
Kun Yan; Lei Ji; Huaishao Luo; Ming Zhou; Nan Duan; Shuai Ma; |
| 158 | Edited Media Understanding Frames: Reasoning About The Intent and Implications of Visual Misinformation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study Edited Media Frames, a new formalism to understand visual media manipulation as structured annotations with respect to the intents, emotional reactions, attacks on individuals, and the overall implications of disinformation. |
Jeff Da; Maxwell Forbes; Rowan Zellers; Anthony Zheng; Jena D. Hwang; Antoine Bosselut; Yejin Choi; |
| 159 | PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in A 3D World Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language. |
Rowan Zellers; Ari Holtzman; Matthew Peters; Roozbeh Mottaghi; Aniruddha Kembhavi; Ali Farhadi; Yejin Choi; |
| 160 | Modeling Fine-Grained Entity Types with Box Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the ability of box embeddings, which embed concepts as d-dimensional hyperrectangles, to capture hierarchies of types even when these relationships are not defined explicitly in the ontology. |
Yasumasa Onoe; Michael Boratko; Andrew McCallum; Greg Durrett; |
| 161 | ChineseBERT: Chinese Pretraining Enhanced By Glyph and Pinyin Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese characters into language model pretraining. |
Zijun Sun; Xiaoya Li; Xiaofei Sun; Yuxian Meng; Xiang Ao; Qing He; Fei Wu; Jiwei Li; |
| 162 | Weight Distillation: Transferring The Knowledge in Neural Network Parameters Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Weight Distillation to transfer the knowledge in parameters of a large neural network to a small neural network through a parameter generator. |
Ye Lin; Yanyang Li; Ziyang Wang; Bei Li; Quan Du; Tong Xiao; Jingbo Zhu; |
| 163 | Optimizing Deeper Transformers on Small Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension. |
Peng Xu; Dhruv Kumar; Wei Yang; Wenjie Zi; Keyi Tang; Chenyang Huang; Jackie Chi Kit Cheung; Simon J.D. Prince; Yanshuai Cao; |
| 164 | BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Of course, we do not have answers now, but, as an attempt to find better neural architectures and training schemes, we pretrain a simple CNN using a GAN-style learning scheme and Wikipedia data, and then integrate it with standard TLMs. |
Jong-Hoon Oh; Ryu Iida; Julien Kloetzer; Kentaro Torisawa; |
| 165 | COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a FEVER-like dataset COVID-Fact of 4,086 claims concerning the COVID-19 pandemic. |
Arkadiy Saakyan; Tuhin Chakrabarty; Smaranda Muresan; |
| 166 | Explaining Relationships Between Scientific Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address the task of explaining relationships between two scientific documents using natural language text. |
Kelvin Luu; Xinyi Wu; Rik Koncel-Kedziorski; Kyle Lo; Isabel Cachola; Noah A. Smith; |
| 167 | IrEne: Interpretable Energy Prediction for Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present IrEne, an interpretable and extensible energy prediction system that accurately predicts the inference energy consumption of a wide range of Transformer-based NLP models. |
Qingqing Cao; Yash Kumar Lal; Harsh Trivedi; Aruna Balasubramanian; Niranjan Balasubramanian; |
| 168 | Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we first show evidence of such bias in models trained on sessions collected from different social media platforms (e.g., Instagram). We then propose a context-aware and model-agnostic debiasing strategy that leverages a reinforcement learning technique, without requiring any extra resources or annotations apart from a pre-defined set of sensitive triggers commonly used for identifying cyberbullying instances. |
Lu Cheng; Ahmadreza Mosallanezhad; Yasin Silva; Deborah Hall; Huan Liu; |
| 169 | PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the new task of synthesizing visualization programs from a combination of natural language utterances and code context. |
Xinyun Chen; Linyuan Gong; Alvin Cheung; Dawn Song; |
| 170 | Changing The World By Changing The Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This position paper maps out the arguments for and against data curation, and argues that fundamentally the point is moot: curation already is and will be happening, and it is changing the world. |
Anna Rogers; |
| 171 | EarlyBERT: Efficient BERT Training Via Early-bird Lottery Tickets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, inspired by the Early-Bird Lottery Tickets recently studied for computer vision tasks, we propose EarlyBERT, a general computationally-efficient training algorithm applicable to both pre-training and fine-tuning of large-scale language models. |
Xiaohan Chen; Yu Cheng; Shuohang Wang; Zhe Gan; Zhangyang Wang; Jingjing Liu; |
| 172 | On The Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We first show that adapter-based tuning better mitigates forgetting issues than fine-tuning since it yields representations with less deviation from those generated by the initial PrLM. |
Ruidan He; Linlin Liu; Hai Ye; Qingyu Tan; Bosheng Ding; Liying Cheng; Jiawei Low; Lidong Bing; Luo Si; |
| 173 | Data Augmentation for Text Generation Without Any Augmented Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we derive an objective to formulate the problem of data augmentation on text generation tasks without any use of augmented data constructed by specific mapping functions. |
Wei Bi; Huayang Li; Jiacheng Huang; |
| 174 | Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we encode the neighborhood information with a graph-induced Gaussian distribution, and propose to integrate the two types of information with a graph-driven generative model. |
Zijing Ou; Qinliang Su; Jianxing Yu; Bang Liu; Jingwen Wang; Ruihui Zhao; Changyou Chen; Yefeng Zheng; |
| 175 | SMURF: SeMantic and Linguistic UndeRstanding Fusion for Caption Evaluation Via Typicality Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce typicality, a new formulation of evaluation rooted in information theory, which is uniquely suited for problems lacking a definite ground truth. |
Joshua Feinglass; Yezhou Yang; |
| 176 | KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we examine the challenges that still prevent these techniques from practical deployment. |
Chia-Hsuan Lee; Oleksandr Polozov; Matthew Richardson; |
| 177 | QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. |
Hamdy Mubarak; Amir Hussein; Shammur Absar Chowdhury; Ahmed Ali; |
| 178 | An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the performance of modern hyperparameter optimization methods (HPO) on fine-tuning pre-trained language models. |
Xueqing Liu; Chi Wang; |
| 179 | Better Than Average: Paired Evaluation of NLP Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we question the use of averages for aggregating evaluation scores into a final number used to decide which system is best, since the average, as well as alternatives such as the median, ignores the pairing arising from the fact that systems are evaluated on the same test instances. |
Maxime Peyrard; Wei Zhao; Steffen Eger; Robert West; |
| 180 | Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present Chase, a large-scale and pragmatic Chinese dataset for XDTS. |
Jiaqi Guo; Ziliang Si; Yu Wang; Qian Liu; Ming Fan; Jian-Guang Lou; Zijiang Yang; Ting Liu; |
| 181 | CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose Contrastive Learning with semantIc Negative Examples (CLINE), which constructs semantic negative examples unsupervised to improve the robustness under semantically adversarial attacking. |
Dong Wang; Ning Ding; Piji Li; Haitao Zheng; |
| 182 | Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we develop a tree-structured topic model by leveraging nonparametric neural variational inference. |
Ziye Chen; Cheng Ding; Zusheng Zhang; Yanghui Rao; Haoran Xie; |
| 183 | ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To facilitate this, we present an Event graph knowledge enhanced explainable CAusal Reasoning framework (ExCAR). |
Li Du; Xiao Ding; Kai Xiong; Ting Liu; Bing Qin; |
| 184 | Distributed Representations of Emotion Categories in Emotion Space Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article, we first propose a general framework to learn the distributed representations for emotion categories in emotion space from a given emotion classification dataset. |
Xiangyu Wang; Chengqing Zong; |
| 185 | Style Is NOT A Single Variable: Case Studies for Cross-Stylistic Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper provides the benchmark corpus (XSLUE) that combines existing datasets and collects a new one for sentence-level cross-style language understanding and evaluation. |
Dongyeop Kang; Eduard Hovy; |
| 186 | DynaSent: A Dynamic Benchmark for Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce DynaSent (‘Dynamic Sentiment’), a new English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. |
Christopher Potts; Zhengxuan Wu; Atticus Geiger; Douwe Kiela; |
| 187 | A Hierarchical VAE for Calibrating Attributes While Generating Text Using Normalizing Flow Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. |
Bidisha Samanta; Mohit Agrawal; NIloy Ganguly; |
| 188 | A Unified Generative Framework for Aspect-based Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexes, which converts all ABSA subtasks into a unified generative formulation. |
Hang Yan; Junqi Dai; Tuo Ji; Xipeng Qiu; Zheng Zhang; |
| 189 | Discovering Dialogue Slots with Weak Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a method that eliminates this requirement: We use weak supervision from existing linguistic annotation models to identify potential slot candidates, then automatically identify domain-relevant slots by using clustering algorithms. |
Vojtech Hudecek; Ondrej Du?ek; Zhou Yu; |
| 190 | Enhancing The Generalization for Intent Classification and Out-of-Domain Detection in SLU Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes to train a model with only IND data while supporting both IND intent classification and OOD detection. |
Yilin Shen; Yen-Chang Hsu; Avik Ray; Hongxia Jin; |
| 191 | ProtAugment: Intent Detection Meta-Learning Through Unsupervised Diverse Paraphrasing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose ProtAugment, a meta-learning algorithm for short texts classification (the intent detection task). |
Thomas Dopierre; Christophe Gravier; Wilfried Logerais; |
| 192 | Robustness Testing of Language Understanding in Task-Oriented Dialog Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we conduct comprehensive evaluation and analysis with respect to the robustness of natural language understanding models, and introduce three important aspects related to language understanding in real-world dialog systems, namely, language variety, speech characteristics, and noise perturbation. |
Jiexi Liu; Ryuichi Takanobu; Jiaxin Wen; Dazhen Wan; Hongguang Li; Weiran Nie; Cheng Li; Wei Peng; Minlie Huang; |
| 193 | Comprehensive Study: How The Context Information of Different Granularity Affects Dialogue State Tracking? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, in this paper, we will study and discuss how the context information of different granularity affects dialogue state tracking. |
Puhai Yang; Heyan Huang; Xian-Ling Mao; |
| 194 | OTTers: One-turn Topic Transitions for Open-Domain Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We first collect a new dataset of human one-turn topic transitions, which we callOTTers. We then explore different strategies used by humans when asked to complete such a task, and notice that the use of a bridging utterance to connect the two topics is the approach used the most. |
Karin Sevegnani; David M. Howcroft; Ioannis Konstas; Verena Rieser; |
| 195 | Towards Robustness of Text-to-SQL Models Against Synonym Substitution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the robustness of text-to-SQL models to synonym substitution. |
Yujian Gan; Xinyun Chen; Qiuping Huang; Matthew Purver; John R. Woodward; Jinxia Xie; Pengsheng Huang; |
| 196 | KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on generating contrastive explanations with counterfactual examples in NLI and propose a novel Knowledge-Aware Contrastive Explanation generation framework (KACE). |
Qianglong Chen; Feng Ji; Xiangji Zeng; Feng-Lin Li; Ji Zhang; Haiqing Chen; Yin Zhang; |
| 197 | Self-Guided Contrastive Learning for BERT Sentence Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a contrastive learning method that utilizes self-guidance for improving the quality of BERT sentence representations. |
Taeuk Kim; Kang Min Yoo; Sang-goo Lee; |
| 198 | LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task. |
Ruisheng Cao; Lu Chen; Zhi Chen; Yanbin Zhao; Su Zhu; Kai Yu; |
| 199 | Multi-stage Pre-training Over Simplified Multimodal Pre-training Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new Multi-stage Pre-training (MSP) method, which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train a model in stages. |
Tongtong Liu; Fangxiang Feng; Xiaojie Wang; |
| 200 | Beyond Sentence-Level End-to-End Speech Translation: Context Helps Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate several decoding approaches, and introduce in-model ensemble decoding which jointly performs document- and sentence-level translation using the same model. |
Biao Zhang; Ivan Titov; Barry Haddow; Rico Sennrich; |
| 201 | LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose LayoutLMv2 architecture with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. |
Yang Xu; Yiheng Xu; Tengchao Lv; Lei Cui; Furu Wei; Guoxin Wang; Yijuan Lu; Dinei Florencio; Cha Zhang; Wanxiang Che; Min Zhang; Lidong Zhou; |
| 202 | UNIMO: Towards Unified-Modal Understanding and Generation Via Cross-Modal Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a UNIfied-MOdal pre-training architecture, namely UNIMO, which can effectively adapt to both single-modal and multi-modal understanding and generation tasks. |
Wei Li; Can Gao; Guocheng Niu; Xinyan Xiao; Hao Liu; Jiachen Liu; Hua Wu; Haifeng Wang; |
| 203 | Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a unified model, Missing Modality Imagination Network (MMIN), to deal with the uncertain missing modality problem. |
Jinming Zhao; Ruichen Li; Qin Jin; |
| 204 | Stacked Acoustic-and-Textual Encoding: Integrating The Pre-trained Models Into Speech Translation Encoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a Stacked Acoustic-and-Textual Encoding (SATE) method for speech translation. |
Chen Xu; Bojie Hu; Yanyang Li; Yuhao Zhang; Shen Huang; Qi Ju; Tong Xiao; Jingbo Zhu; |
| 205 | N-ary Constituent Tree Parsing with Recursive Semi-Markov Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children. |
Xin Xin; Jinlong Li; Zeqi Tan; |
| 206 | Automated Concatenation of Embeddings for Structured Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search. |
Xinyu Wang; Yong Jiang; Nguyen Bach; Tao Wang; Zhongqiang Huang; Fei Huang; Kewei Tu; |
| 207 | Multi-View Cross-Lingual Structured Prediction with Minimum Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a multi-view framework, by leveraging a small number of labeled target sentences, to effectively combine multiple source models into an aggregated source view at different granularity levels (language, sentence, or sub-structure), and transfer it to a target view based on a task-specific model. |
Zechuan Hu; Yong Jiang; Nguyen Bach; Tao Wang; Zhongqiang Huang; Fei Huang; Kewei Tu; |
| 208 | The Limitations of Limited Context for Constituency Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we answer representational questions raised by the architectures in (Shen et al., 2018a, 2019), as well as some transition-based syntax-aware language models (Dyer et al., 2016): what kind of syntactic structure can current neural approaches to syntax represent? |
Yuchen Li; Andrej Risteski; |
| 209 | Neural Bi-Lexicalized PCFG Induction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an approach to parameterize L-PCFGs without making implausible independence assumptions. |
Songlin Yang; Yanpeng Zhao; Kewei Tu; |
| 210 | Ruddit: Norms of Offensiveness for English Reddit Comments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We create the first dataset of English language Reddit comments that has fine-grained, real-valued scores between -1 (maximally supportive) and 1 (maximally offensive). |
Rishav Hada; Sohi Sudhir; Pushkar Mishra; Helen Yannakoudakis; Saif M. Mohammad; Ekaterina Shutova; |
| 211 | Towards Quantifiable Dialogue Coherence Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these limitations, we propose Quantifiable Dialogue Coherence Evaluation (QuantiDCE), a novel framework aiming to train a quantifiable dialogue coherence metric that can reflect the actual human rating standards. |
Zheng Ye; Liucun Lu; Lishan Huang; Liang Lin; Xiaodan Liang; |
| 212 | Assessing The Representations of Idiomaticity in Vector Models with A Noun Compound Dataset Labeled at Type and Token Levels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents the Noun Compound Type and Token Idiomaticity (NCTTI) dataset, with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token level. |
Marcos Garcia; Tiago Kramer Vieira; Carolina Scarton; Marco Idiart; Aline Villavicencio; |
| 213 | Factoring Statutory Reasoning As Language Understanding Challenges Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Prior work introduced a resource that approached statutory reasoning as a monolithic textual entailment problem, with neural baselines performing nearly at-chance. |
Nils Holzenberger; Benjamin Van Durme; |
| 214 | Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the present study, we utilise data from the SemEval and NTCIR communities to clarify the properties of nine evaluation measures in the context of OC tasks, and six measures in the context of OQ tasks. |
Tetsuya Sakai; |
| 215 | Interpretable and Low-Resource Entity Matching Via Decoupling Feature Learning from Decision Making Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel EM framework that consists of Heterogeneous Information Fusion (HIF) and Key Attribute Tree (KAT) Induction to decouple feature representation from matching decision. |
Zijun Yao; Chengjiang Li; Tiansi Dong; Xin Lv; Jifan Yu; Lei Hou; Juanzi Li; Yichi Zhang; Zelin Dai; |
| 216 | Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: First we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundary-adjusted span proposals with the corresponding categories. |
Yongliang Shen; Xinyin Ma; Zeqi Tan; Shuai Zhang; Wen Wang; Weiming Lu; |
| 217 | Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner. |
Yaojie Lu; Hongyu Lin; Jin Xu; Xianpei Han; Jialong Tang; Annan Li; Le Sun; Meng Liao; Shaoyi Chen; |
| 218 | A Large-Scale Chinese Multimodal NER Dataset with Speech Clues Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents. |
Dianbo Sui; Zhengkun Tian; Yubo Chen; Kang Liu; Jun Zhao; |
| 219 | A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a neural transition-based joint model to alleviate these two issues. |
Zongcheng Ji; Tian Xia; Mei Han; Jing Xiao; |
| 220 | OntoED: Low-resource Event Detection with Ontology Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. |
Shumin Deng; Ningyu Zhang; Luoqiu Li; Chen Hui; Tou Huaixiao; Mosha Chen; Fei Huang; Huajun Chen; |
| 221 | Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data. |
Wenxiang Jiao; Xing Wang; Zhaopeng Tu; Shuming Shi; Michael Lyu; Irwin King; |
| 222 | Breaking The Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To break the corpus bottleneck, in this paper we aim to improve context-aware NMT by taking the advantage of the availability of both large-scale sentence-level parallel dataset and source-side monolingual documents. |
Linqing Chen; Junhui Li; Zhengxian Gong; Boxing Chen; Weihua Luo; Min Zhang; Guodong Zhou; |
| 223 | Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we introduce another decoder, called seer decoder, into the encoder-decoder framework during training, which involves future information in target predictions. |
Yang Feng; Shuhao Gu; Dengji Guo; Zhengxin Yang; Chenze Shao; |
| 224 | Cascade Versus Direct Speech Translation: Do The Differences Still Make A Difference? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Starting from this question, we present a systematic comparison between state-of-the-art systems representative of the two paradigms. |
Luisa Bentivogli; Mauro Cettolo; Marco Gaido; Alina Karakanta; Alberto Martinelli; Matteo Negri; Marco Turchi; |
| 225 | Unsupervised Neural Machine Translation for Low-Resource Domains Via Meta-Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data. |
Cheonbok Park; Yunwon Tae; TaeHee Kim; Soyoung Yang; Mohammad Azam Khan; Lucy Park; Jaegul Choo; |
| 226 | Lightweight Cross-Lingual Sentence Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a lightweight dual-transformer architecture with just 2 layers for generating memory-efficient cross-lingual sentence representations. |
Zhuoyuan Mao; Prakhar Gupta; Chenhui Chu; Martin Jaggi; Sadao Kurohashi; |
| 227 | ERNIE-Doc: A Retrospective Long-Document Modeling Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose ERNIE-Doc, a document-level language pretraining model based on Recurrence Transformers. |
SiYu Ding; Junyuan Shang; Shuohuan Wang; Yu Sun; Hao Tian; Hua Wu; Haifeng Wang; |
| 228 | Marginal Utility Diminishes: Exploring The Minimum Knowledge for BERT Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this way, we show that 1) the student’s performance can be improved by extracting and distilling the crucial HSK, and 2) using a tiny fraction of HSK can achieve the same performance as extensive HSK distillation. |
Yuanxin Liu; Fandong Meng; Zheng Lin; Weiping Wang; Jie Zhou; |
| 229 | Rational LAMOL: A Rationale-based Lifelong Learning Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce Rational LAMOL, a novel end-to-end LL framework for language models. |
Kasidis Kanwatchara; Thanapapas Horsuwan; Piyawat Lertvittayakumjorn; Boonserm Kijsirikul; Peerapon Vateekul; |
| 230 | EnsLM: Ensemble Language Model for Data Diversity By Semantic Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Having obtained the clustering assignment for each sample, we develop the ensemble LM (EnsLM) with the technique of weight modulation. |
Zhibin Duan; Hao Zhang; Chaojie Wang; Zhengjue Wang; Bo Chen; Mingyuan Zhou; |
| 231 | LeeBERT: Learned Early Exit for BERT with Cross-level Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, to improve efficiency without performance drop, we propose a novel training scheme called Learned Early Exit for BERT (LeeBERT). |
Wei Zhu; |
| 232 | Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We pioneer the first extractive summarization-based collaborative filtering model called ESCOFILT. |
Reinald Adrian Pugoy; Hung-Yu Kao; |
| 233 | PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) for CSC, which jointly learns how to understand language and correct spelling errors. |
Shulin Liu; Tao Yang; Tianchi Yue; Feng Zhang; Di Wang; |
| 234 | Competence-based Multimodal Curriculum Learning for Medical Report Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate the data bias and make best use of available data, we propose a Competence-based Multimodal Curriculum Learning framework (CMCL). |
Fenglin Liu; Shen Ge; Xian Wu; |
| 235 | Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to incorporate linguistic features into neural network models by learning syntactic dense embeddings based on linguistic features. |
Xinying Qiu; Yuan Chen; Hanwu Chen; Jian-Yun Nie; Yuming Shen; Dawei Lu; |
| 236 | Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression Across Domains Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence we propose a Meta-Knowledge Distillation (Meta-KD) framework to build a meta-teacher model that captures transferable knowledge across domains and passes such knowledge to students. |
Haojie Pan; Chengyu Wang; Minghui Qiu; Yichang Zhang; Yaliang Li; Jun Huang; |
| 237 | A Semantic-based Method for Unsupervised Commonsense Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel SEmantic-based Question Answering method (SEQA) for unsupervised commonsense question answering. |
Yilin Niu; Fei Huang; Jiaming Liang; Wenkai Chen; Xiaoyan Zhu; Minlie Huang; |
| 238 | Explanations for CommonsenseQA: New Dataset and Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our explanation definition is based on certain desiderata, and translates an explanation into a set of positive and negative common-sense properties (aka facts) which not only explain the correct answer choice but also refute the incorrect ones. |
Shourya Aggarwal; Divyanshu Mandowara; Vishwajeet Agrawal; Dinesh Khandelwal; Parag Singla; Dinesh Garg; |
| 239 | Few-Shot Question Answering By Pretraining Span Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new pretraining scheme tailored for question answering: recurring span selection. |
Ori Ram; Yuval Kirstain; Jonathan Berant; Amir Globerson; Omer Levy; |
| 240 | UnitedQA: A Hybrid Approach for Open Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To date, most of recent work under the retrieval-reader framework for open-domain QA focuses on either extractive or generative reader exclusively. In this paper, we study a hybrid approach for leveraging the strengths of both models. |
Hao Cheng; Yelong Shen; Xiaodong Liu; Pengcheng He; Weizhu Chen; Jianfeng Gao; |
| 241 | Database Reasoning Over Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a modular architecture to answer these database-style queries over multiple spans from text and aggregating these at scale. |
James Thorne; Majid Yazdani; Marzieh Saeidi; Fabrizio Silvestri; Sebastian Riedel; Alon Halevy; |
| 242 | Online Learning Meets Machine Translation Evaluation: Finding The Best Systems with The Least Human Effort Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the latter challenge, we propose a novel application of online learning that, given an ensemble of Machine Translation systems, dynamically converges to the best systems, by taking advantage of the human feedback available. |
V?nia Mendon?a; Ricardo Rei; Luisa Coheur; Alberto Sardinha; Ana L?cia Santos; |
| 243 | How Good Is Your Tokenizer? On The Monolingual Performance of Multilingual Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance. |
Phillip Rust; Jonas Pfeiffer; Ivan Vulic; Sebastian Ruder; Iryna Gurevych; |
| 244 | Evaluating Morphological Typology in Zero-shot Cross-lingual Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address what effects morphological typology has on zero-shot cross-lingual transfer for two tasks: Part-of-speech tagging and sentiment analysis. |
Antonio Mart?nez-Garc?a; Toni Badia; Jeremy Barnes; |
| 245 | From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we adapt a state-of-the-art neural machine translation model to generate Hindi-English code-switched sentences starting from monolingual Hindi sentences. |
Ishan Tarunesh; Syamantak Kumar; Preethi Jyothi; |
| 246 | Fast and Accurate Neural Machine Translation with Translation Memory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a fast and accurate approach to TM-based NMT within the Transformer framework: the model architecture is simple and employs a single bilingual sentence as its TM, leading to efficient training and inference; and its parameters are effectively optimized through a novel training criterion. |
Qiuxiang He; Guoping Huang; Qu Cui; Li Li; Lemao Liu; |
| 247 | Annotating Online Misogyny Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper makes three contributions in this area: Firstly, we describe the detailed design of our iterative annotation process and codebook. Secondly, we present a comprehensive taxonomy of labels for annotating misogyny in natural written language, and finally, we introduce a high-quality dataset of annotated posts sampled from social media posts. |
Philine Zeinert; Nanna Inie; Leon Derczynski; |
| 248 | Few-NERD: A Few-shot Named Entity Recognition Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types. |
Ning Ding; Guangwei Xu; Yulin Chen; Xiaobin Wang; Xu Han; Pengjun Xie; Haitao Zheng; Zhiyuan Liu; |
| 249 | MultiMET: A Multimodal Dataset for Metaphor Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce MultiMET, a novel multimodal metaphor dataset to facilitate understanding metaphorical information from multimodal text and image. |
Dongyu Zhang; Minghao Zhang; Heting Zhang; Liang Yang; Hongfei Lin; |
| 250 | Human-in-the-Loop for Data Collection: A Multi-Target Counter Narrative Dataset to Fight Online Hate Speech Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel human-in-the-loop data collection methodology in which a generative language model is refined iteratively by using its own data from the previous loops to generate new training samples that experts review and/or post-edit. |
Margherita Fanton; Helena Bonaldi; Serra Sinem Tekiroglu; Marco Guerini; |
| 251 | Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We construct a new dataset of closed-book QA using SQuAD, and investigate the performance of BART. |
Cunxiang Wang; Pai Liu; Yue Zhang; |
| 252 | Joint Models for Answer Verification in Question Answering Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper studies joint models for selecting correct answer sentences among the top k provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems. |
Zeyu Zhang; Thuy Vu; Alessandro Moschitti; |
| 253 | Answering Ambiguous Questions Through Generative Evidence Fusion and Round-Trip Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a model that aggregates and combines evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions. |
Yifan Gao; Henghui Zhu; Patrick Ng; Cicero Nogueira dos Santos; Zhiguo Wang; Feng Nan; Dejiao Zhang; Ramesh Nallapati; Andrew O. Arnold; Bing Xiang; |
| 254 | TAT-QA: A Question Answering Benchmark on A Hybrid of Tabular and Textual Content in Finance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions. |
Fengbin Zhu; Wenqiang Lei; Youcheng Huang; Chao Wang; Shuo Zhang; Jiancheng Lv; Fuli Feng; Tat-Seng Chua; |
| 255 | Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. |
Yunshi Lan; Jing Jiang; |
| 256 | Evidence-based Factual Error Correction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence. |
James Thorne; Andreas Vlachos; |
| 257 | Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present algorithms for aligning components of Abstract Meaning Representation (AMR) graphs to spans in English sentences. |
Austin Blodgett; Nathan Schneider; |
| 258 | Meta-Learning to Compositionally Generalize Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We implement a meta-learning augmented version of supervised learning whose objective directly optimizes for out-of-distribution generalization. |
Henry Conklin; Bailin Wang; Kenny Smith; Ivan Titov; |
| 259 | Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to adapt a generic pretrained model with a relatively small amount of domain-specific data. |
Shizhe Diao; Ruijia Xu; Hongjin Su; Yilei Jiang; Yan Song; Tong Zhang; |
| 260 | ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models Via Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel contrastive learning framework ERICA to obtain a deep understanding of the entities and their relations in text. |
Yujia Qin; Yankai Lin; Ryuichi Takanobu; Zhiyuan Liu; Peng Li; Heng Ji; Minlie Huang; Maosong Sun; Jie Zhou; |
| 261 | Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the dataset bias, we propose a novel graph-based method to explicitly model the emotion triggering paths by leveraging the commonsense knowledge to enhance the semantic dependencies between a candidate clause and an emotion clause. |
Hanqi Yan; Lin Gui; Gabriele Pergola; Yulan He; |
| 262 | Every Bite Is An Experience: Key Point Analysis of Business Reviews Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata. |
Roy Bar-Haim; Lilach Eden; Yoav Kantor; Roni Friedman; Noam Slonim; |
| 263 | Structured Sentiment Analysis As Dependency Graph Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We argue that this division has become counterproductive and propose a new unified framework to remedy the situation. |
Jeremy Barnes; Robin Kurtz; Stephan Oepen; Lilja ?vrelid; Erik Velldal; |
| 264 | Consistency Regularization for Cross-Lingual Fine-Tuning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to improve cross-lingual fine-tuning with consistency regularization. |
Bo Zheng; Li Dong; Shaohan Huang; Wenhui Wang; Zewen Chi; Saksham Singhal; Wanxiang Che; Ting Liu; Xia Song; Furu Wei; |
| 265 | Improving Pretrained Cross-Lingual Language Models Via Self-Labeled Word Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce denoising word alignment as a new cross-lingual pre-training task. |
Zewen Chi; Li Dong; Bo Zheng; Shaohan Huang; Xian-Ling Mao; Heyan Huang; Furu Wei; |
| 266 | Rejuvenating Low-Frequency Words: Making The Most of Parallel Data in Non-Autoregressive Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, we propose reverse KD to rejuvenate more alignments for low-frequency target words. |
Liang Ding; Longyue Wang; Xuebo Liu; Derek F. Wong; Dacheng Tao; Zhaopeng Tu; |
| 267 | G-Transformer for Document-Level Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As a solution, we propose G-Transformer, introducing locality assumption as an inductive bias into Transformer, reducing the hypothesis space of the attention from target to source. |
Guangsheng Bao; Yue Zhang; Zhiyang Teng; Boxing Chen; Weihua Luo; |
| 268 | Prevent The Language Model from Being Overconfident in Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the property, we propose a Margin-based Token-level Objective (MTO) and a Margin-based Sentence-level Objective (MSO) to maximize the Margin for preventing the LM from being overconfident. |
Mengqi Miao; Fandong Meng; Yijin Liu; Xiao-Hua Zhou; Jie Zhou; |
| 269 | Towards Emotional Support Dialog Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we define the Emotional Support Conversation (ESC) task and propose an ESC Framework, which is grounded on the Helping Skills Theory. |
Siyang Liu; Chujie Zheng; Orianna Demasi; Sahand Sabour; Yu Li; Zhou Yu; Yong Jiang; Minlie Huang; |
| 270 | Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in The Task-Oriented Dialogue System Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a new task, Novel Slot Detection (NSD), in the task-oriented dialogue system. |
Yanan Wu; Zhiyuan Zeng; Keqing He; Hong Xu; Yuanmeng Yan; Huixing Jiang; Weiran Xu; |
| 271 | GTM: A Generative Triple-wise Model for Conversational Question Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these problems, we propose a generative triple-wise model with hierarchical variations for open-domain conversational question generation (CQG). |
Lei Shen; Fandong Meng; Jinchao Zhang; Yang Feng; Jie Zhou; |
| 272 | Diversifying Dialog Generation Via Adaptive Label Smoothing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an Adaptive Label Smoothing (AdaLabel) approach that can adaptively estimate a target label distribution at each time step for different contexts. |
Yida Wang; Yinhe Zheng; Yong Jiang; Minlie Huang; |
| 273 | Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple yet effective method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training, which requires no assumption on data distribution and no additional post-processing or threshold setting. |
Li-Ming Zhan; Haowen Liang; Bo Liu; Lu Fan; Xiao-Ming Wu; Albert Y.S. Lam; |
| 274 | Document-level Event Extraction Via Heterogeneous Graph-based Interaction Model with A Tracker Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Heterogeneous Graph-based Interaction Model with a Tracker (GIT) to solve the aforementioned two challenges. |
Runxin Xu; Tianyu Liu; Lei Li; Baobao Chang; |
| 275 | Nested Named Entity Recognition Via Explicitly Excluding The Influence of The Best Path Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel method for nested named entity recognition. |
Yiran Wang; Hiroyuki Shindo; Yuji Matsumoto; Taro Watanabe; |
| 276 | LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the data lacking problem, we introduce a new approach to augment training data for event causality identification, by iteratively generating new examples and classifying event causality in a dual learning framework. |
Xinyu Zuo; Pengfei Cao; Yubo Chen; Kang Liu; Jun Zhao; Weihua Peng; Yuguang Chen; |
| 277 | Revisiting The Negative Data of Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we first provide a thorough analysis of the above challenges caused by negative data. Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem. |
Chenhao Xie; Jiaqing Liang; Jingping Liu; Chengsong Huang; Wenhao Huang; Yanghua Xiao; |
| 278 | Knowing The No-match: Entity Alignment with Dangling Cases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). |
Zequn Sun; Muhao Chen; Wei Hu; |
| 279 | Superbizarre Is Not Superb: Derivational Morphology Improves BERT’s Interpretation of Complex Words Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present the first study investigating this question, taking BERT as the example PLM and focusing on its semantic representations of English derivatives. |
Valentin Hofmann; Janet Pierrehumbert; Hinrich Sch?tze; |
| 280 | BERT Is to NLP What AlexNet Is to CV: Can Pre-Trained Language Models Identify Analogies? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we analyze the capabilities of transformer-based language models on this unsupervised task, using benchmarks obtained from educational settings, as well as more commonly used datasets. |
Asahi Ushio; Luis Espinosa Anke; Steven Schockaert; Jose Camacho-Collados; |
| 281 | Exploring The Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a multilingual study of word meaning representations in context. |
Marcos Garcia; |
| 282 | Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to measure fine-grained domain relevance- the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning) domain. |
Jie Huang; Kevin Chang; JinJun Xiong; Wen-mei Hwu; |
| 283 | HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose HERALD, an efficient annotation framework that reframes the training data annotation process as a denoising problem. |
Weixin Liang; Kai-Hui Liang; Zhou Yu; |
| 284 | Value-Agnostic Conversational Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a model that abstracts over values to focus prediction on type- and function-level context. |
Emmanouil Antonios Platanios; Adam Pauls; Subhro Roy; Yuchen Zhang; Alexander Kyte; Alan Guo; Sam Thomson; Jayant Krishnamurthy; Jason Wolfe; Jacob Andreas; Dan Klein; |
| 285 | MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we present MPC-BERT, a pre-trained model for MPC understanding that considers learning who says what to whom in a unified model with several elaborated self-supervised tasks. |
Jia-Chen Gu; Chongyang Tao; Zhenhua Ling; Can Xu; Xiubo Geng; Daxin Jiang; |
| 286 | Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address the challenge of introducing methods for word-by-word left-to-right incremental processing to Transformers such as BERT, models without an intrinsic sense of linear order. |
Morteza Rohanian; Julian Hough; |
| 287 | NeuralWOZ: Learning to Collect Task-Oriented Dialogue Via Model-Based Simulation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation. |
Sungdong Kim; Minsuk Chang; Sang-Woo Lee; |
| 288 | CDRNN: Discovering Complex Dynamics in Human Language Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study proposes the continuous-time deconvolutional regressive neural network (CDRNN), a deep neural extension of continuous-time deconvolutional regression (Shain & Schuler, 2021) that jointly captures time-varying, non-linear, and delayed influences of predictors (e.g. word surprisal) on the response (e.g. reading time). |
Cory Shain; |
| 289 | Structural Guidance for Transformer Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we study whether structural guidance leads to more human-like systematic linguistic generalization in Transformer language models without resorting to pre-training on very large amounts of data. |
Peng Qian; Tahira Naseem; Roger Levy; Ram?n Fernandez Astudillo; |
| 290 | Surprisal Estimators for Human Reading Times Need Character Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a character model that can be applied to a structural parser-based processing model to calculate word generation probabilities. |
Byung-Doh Oh; Christian Clark; William Schuler; |
| 291 | CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a CogAlign approach to these issues, which learns to align textual neural representations to cognitive features. |
Yuqi Ren; Deyi Xiong; |
| 292 | Self-Attention Networks Can Process Bounded Hierarchical Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we construct a hard-attention network with D+1 layers and O(log k) memory size (per token per layer) that recognizes Dyck-(k, D), and a soft-attention network with two layers and O(log k) memory size that generates Dyck-(k, D). |
Shunyu Yao; Binghui Peng; Christos Papadimitriou; Karthik Narasimhan; |
| 293 | TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel approach to the problem of text style transfer. |
Parker Riley; Noah Constant; Mandy Guo; Girish Kumar; David Uthus; Zarana Parekh; |
| 294 | H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe an efficient hierarchical method to compute attention in the Transformer architecture. |
Zhenhai Zhu; Radu Soricut; |
| 295 | Making Pre-trained Language Models Better Few-shot Learners Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present LM-BFF-better few-shot fine-tuning of language models-a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples. |
Tianyu Gao; Adam Fisch; Danqi Chen; |
| 296 | A Sweet Rabbit Hole By DARCY: Using Honeypots to Detect Universal Trigger’s Adversarial Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To defend against this attack that can cause significant harm, in this paper, we borrow the honeypot concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework against UniTrigger. |
Thai Le; Noseong Park; Dongwon Lee; |
| 297 | Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a novel Edge-enhanced Bayesian Graph Convolutional Network (EBGCN) to capture robust structural features. |
Lingwei Wei; Dou Hu; Wei Zhou; Zhaojuan Yue; Songlin Hu; |
| 298 | Label-Specific Dual Graph Neural Network for Multi-Label Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel label-specific dual graph neural network (LDGN), which incorporates category information to learn label-specific components from documents, and employs dual Graph Convolution Network (GCN) to model complete and adaptive interactions among these components based on the statistical label co-occurrence and dynamic reconstruction graph in a joint way. |
Qianwen Ma; Chunyuan Yuan; Wei Zhou; Songlin Hu; |
| 299 | TAN-NTM: Topic Attention Networks for Neural Topic Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel attention mechanism which factors in topic-word distribution to enable the model to attend on relevant words that convey topic related cues. |
Madhur Panwar; Shashank Shailabh; Milan Aggarwal; Balaji Krishnamurthy; |
| 300 | Cross-language Sentence Selection Via Data Augmentation and Rationale Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an approach to cross-language sentence selection in a low-resource setting. |
Yanda Chen; Chris Kedzie; Suraj Nair; Petra Galuscakova; Rui Zhang; Douglas Oard; Kathleen McKeown; |
| 301 | A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an architecture for joint document and snippet ranking, the two middle stages, which leverages the intuition that relevant documents have good snippets and good snippets come from relevant documents. |
Dimitris Pappas; Ion Androutsopoulos; |
| 302 | W-RST: Towards A Weighted RST-style Discourse Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary assessment of importance between text segments, can be replaced by automatically generated, real-valued scores, in what we call a Weighted-RST framework. |
Patrick Huber; Wen Xiao; Giuseppe Carenini; |
| 303 | ABCD: A Graph Framework to Convert Complex Sentences to A Covering Set of Simple Sentences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task. |
Yanjun Gao; Ting-Hao Huang; Rebecca J. Passonneau; |
| 304 | Which Linguist Invented The Lightbulb? Presupposition Verification for Question-Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Through a user preference study, we demonstrate that the oracle behavior of our proposed system-which provides responses based on presupposition failure-is preferred over the oracle behavior of existing QA systems. Then, we present a novel framework for implementing such a system in three steps: presupposition generation, presupposition verification, and explanation generation, reporting progress on each. |
Najoung Kim; Ellie Pavlick; Burcu Karagol Ayan; Deepak Ramachandran; |
| 305 | Adversarial Learning for Discourse Rhetorical Structure Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present our insight on evaluating the pros and cons of the entire DRS tree for global optimization. |
Longyin Zhang; Fang Kong; Guodong Zhou; |
| 306 | Exploring Discourse Structures for Argument Impact Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument. |
Xin Liu; Jiefu Ou; Yangqiu Song; Xin Jiang; |
| 307 | Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models. |
Tong Zhang; Long Zhang; Wei Ye; Bo Li; Jinan Sun; Xiaoyu Zhu; Wen Zhao; Shikun Zhang; |
| 308 | VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages. |
Fuli Luo; Wei Wang; Jiahao Liu; Yijia Liu; Bin Bi; Songfang Huang; Fei Huang; Luo Si; |
| 309 | A Unified Approach to Sentence Segmentation of Punctuated Text in Many Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a modern context-based modeling approach that provides a solution to the problem of segmenting punctuated text in many languages, and show how it can be trained on noisily-annotated data. |
Rachel Wicks; Matt Post; |
| 310 | Towards User-Driven Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this gap, we introduce a novel framework called user-driven NMT. |
Huan Lin; Liang Yao; Baosong Yang; Dayiheng Liu; Haibo Zhang; Weihua Luo; Degen Huang; Jinsong Su; |
| 311 | End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we focus on methods based on training the model with constraints provided as part of the input sequence. |
Josef Jon; Jo?o Paulo Aires; Dusan Varis; Ondrej Bojar; |
| 312 | Handling Extreme Class Imbalance in Technical Logbook Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we focus on the problem of technical issue classification by considering logbook datasets from the automotive, aviation, and facilities maintenance domains. |
Farhad Akhbardeh; Cecilia Ovesdotter Alm; Marcos Zampieri; Travis Desell; |
| 313 | ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To promote research in developing such a system, we introduce ILDC (Indian Legal Documents Corpus). |
Vijit Malik; Rishabh Sanjay; Shubham Kumar Nigam; Kripabandhu Ghosh; Shouvik Kumar Guha; Arnab Bhattacharya; Ashutosh Modi; |
| 314 | Supporting Cognitive and Emotional Empathic Writing of Students Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German. |
Thiemo Wambsganss; Christina Niklaus; Matthias S?llner; Siegfried Handschuh; Jan Marco Leimeister; |
| 315 | Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a hybrid framework that takes both textual and tabular evidences as input and generates either direct answers or SQL queries depending on which form could better answer the question. |
Alexander Hanbo Li; Patrick Ng; Peng Xu; Henghui Zhu; Zhiguo Wang; Bing Xiang; |
| 316 | Generation-Augmented Retrieval for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. |
Yuning Mao; Pengcheng He; Xiaodong Liu; Yelong Shen; Jianfeng Gao; Jiawei Han; Weizhu Chen; |
| 317 | Check It Again:Progressive Visual Question Answering Via Visual Entailment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a select-and-rerank (SAR) progressive framework based on Visual Entailment. |
Qingyi Si; Zheng Lin; Ming yu Zheng; Peng Fu; Weiping Wang; |
| 318 | A Mutual Information Maximization Approach for The Spurious Solution Problem in Weakly Supervised Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to alleviate the spurious solution problem, we propose to explicitly exploit such semantic correlations by maximizing the mutual information between question-answer pairs and predicted solutions. |
Zhihong Shao; Lifeng Shang; Qun Liu; Minlie Huang; |
| 319 | Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to provide a roadmap for the development and use of language technologies to empower users to reclaim control over their privacy, limit privacy harms, and rally research efforts from the community towards addressing an issue with large social impact. |
Abhilasha Ravichander; Alan W Black; Thomas Norton; Shomir Wilson; Norman Sadeh; |
| 320 | Supporting Land Reuse of Former Open Pit Mining Sites Using Text Classification and Active Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we present and evaluate an automated workflow for supporting the post-mining management of former lignite open pit mines in the eastern part of Germany, where prior to any planned land reuse, aforementioned information has to be acquired to ensure the safety and validity of such an endeavor. |
Christopher Schr?der; Kim B?rgl; Yves Annanias; Andreas Niekler; Lydia M?ller; Daniel Wiegreffe; Christian Bender; Christoph Mengs; Gerik Scheuermann; Gerhard Heyer; |
| 321 | Reliability Testing for Natural Language Processing Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we argue for the need for reliability testing and contextualize it among existing work on improving accountability. |
Samson Tan; Shafiq Joty; Kathy Baxter; Araz Taeihagh; Gregory A. Bennett; Min-Yen Kan; |
| 322 | Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors. |
Paul Pu Liang; Terrance Liu; Anna Cai; Michal Muszynski; Ryo Ishii; Nick Allen; Randy Auerbach; David Brent; Ruslan Salakhutdinov; Louis-Philippe Morency; |
| 323 | Anonymisation Models for Text Data: State of The Art, Challenges and Future Directions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals. |
Pierre Lison; Ildik? Pil?n; David Sanchez; Montserrat Batet; Lilja ?vrelid; |
| 324 | End-to-End AMR Corefencence Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the first end-to-end AMR coreference resolution model in order to build multi-sentence AMRs. |
Qiankun Fu; Linfeng Song; Wenyu Du; Yue Zhang; |
| 325 | How Is BERT Surprised? Layerwise Detection of Linguistic Anomalies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we use Gaussian models for density estimation at intermediate layers of three language models (BERT, RoBERTa, and XLNet), and evaluate our method on BLiMP, a grammaticality judgement benchmark. |
Bai Li; Zining Zhu; Guillaume Thomas; Yang Xu; Frank Rudzicz; |
| 326 | Psycholinguistic Tripartite Graph Network for Personality Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a psycholinguistic knowledge-based tripartite graph network, TrigNet, which consists of a tripartite graph network and a BERT-based graph initializer. |
Tao Yang; Feifan Yang; Haolan Ouyang; Xiaojun Quan; |
| 327 | Verb Metaphor Detection Via Contextual Relation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that it is appropriate to view this task as relation classification between a verb and its various contexts. |
Wei Song; Shuhui Zhou; Ruiji Fu; Ting Liu; Lizhen Liu; |
| 328 | Improving Speech Translation By Understanding and Learning from The Auxiliary Text Translation Task Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we are interested in training a speech translation model along with an auxiliary text translation task. |
Yun Tang; Juan Pino; Xian Li; Changhan Wang; Dmitriy Genzel; |
| 329 | Probing Toxic Content in Large Pre-Trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a method based on logistic regression classifiers to probe English, French, and Arabic PTLMs and quantify the potentially harmful content that they convey with respect to a set of templates. |
Nedjma Ousidhoum; Xinran Zhao; Tianqing Fang; Yangqiu Song; Dit-Yan Yeung; |
| 330 | Societal Biases in Language Generation: Progress and Challenges Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better understand these challenges, we present a survey on societal biases in language generation, focusing on how data and techniques contribute to biases and progress towards reducing biases. |
Emily Sheng; Kai-Wei Chang; Prem Natarajan; Nanyun Peng; |
| 331 | Reservoir Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by old and well-established ideas in machine learning, we explore a variety of non-linear reservoir layers interspersed with regular transformer layers, and show improvements in wall-clock compute time until convergence, as well as overall performance, on various machine translation and (masked) language modelling tasks. |
Sheng Shen; Alexei Baevski; Ari Morcos; Kurt Keutzer; Michael Auli; Douwe Kiela; |
| 332 | Subsequence Based Deep Active Learning for Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we overcome these limitations by allowing the AL algorithm to query subsequences within sentences, and propagate their labels to other sentences. |
Puria Radmard; Yassir Fathullah; Aldo Lipani; |
| 333 | Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we detail the relationship between convolutions and self-attention in natural language tasks. |
Tyler Chang; Yifan Xu; Weijian Xu; Zhuowen Tu; |
| 334 | BinaryBERT: Pushing The Limit of BERT Quantization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose BinaryBERT, which pushes BERT quantization to the limit by weight binarization. |
Haoli Bai; Wei Zhang; Lu Hou; Lifeng Shang; Jin Jin; Xin Jiang; Qun Liu; Michael Lyu; Irwin King; |
| 335 | Are Pretrained Convolutions Better Than Pretrained Transformers? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Overall, the findings outlined in this paper suggest that conflating pre-training and architectural advances is misguided and that both advances should be considered independently. |
Yi Tay; Mostafa Dehghani; Jai Prakash Gupta; Vamsi Aribandi; Dara Bahri; Zhen Qin; Donald Metzler; |
| 336 | PairRE: Knowledge Graph Embeddings Via Paired Relation Vectors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate this problem, we propose PairRE, a model with paired vectors for each relation representation. |
Linlin Chao; Jianshan He; Taifeng Wang; Wei Chu; |
| 337 | Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we formulate the text-label semantics relationship as a semantic matching problem and thus propose a hierarchy-aware label semantics matching network (HiMatch). |
Haibin Chen; Qianli Ma; Zhenxi Lin; Jiangyue Yan; |
| 338 | HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizability Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a simple yet effective data augmentation technique, HiddenCut, to better regularize the model and encourage it to learn more generalizable features. |
Jiaao Chen; Dinghan Shen; Weizhu Chen; Diyi Yang; |
| 339 | Neural Stylistic Response Generation with Disentangled Latent Variables Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to disentangle the content and style in latent space by diluting sentence-level information in style representations. |
Qingfu Zhu; Wei-Nan Zhang; Ting Liu; William Yang Wang; |
| 340 | Intent Classification and Slot Filling for Privacy Policies Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present two alternative neural approaches as baselines, (1) intent classification and slot filling as a joint sequence tagging and (2) modeling them as a sequence-to-sequence (Seq2Seq) learning task. |
Wasi Ahmad; Jianfeng Chi; Tu Le; Thomas Norton; Yuan Tian; Kai-Wei Chang; |
| 341 | RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In pursuit of these goals, we introduce the RADDLE benchmark, a collection of corpora and tools for evaluating the performance of models across a diverse set of domains. |
Baolin Peng; Chunyuan Li; Zhu Zhang; Chenguang Zhu; Jinchao Li; Jianfeng Gao; |
| 342 | Semantic Representation for Dialogue Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems. |
Xuefeng Bai; Yulong Chen; Linfeng Song; Yue Zhang; |
| 343 | A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the challenge, we consider decomposing the training of the knowledge-grounded response selection into three tasks including: 1) query-passage matching task; 2) query-dialogue history matching task; 3) multi-turn response matching task, and joint learning all these tasks in a unified pre-trained language model. |
Chongyang Tao; Changyu Chen; Jiazhan Feng; Ji-Rong Wen; Rui Yan; |
| 344 | Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a dependency-driven approach for relation extraction with attentive graph convolutional networks (A-GCN). |
Yuanhe Tian; Guimin Chen; Yan Song; Xiang Wan; |
| 345 | Evaluating Entity Disambiguation and The Role of Popularity in Retrieval-Based NLP Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an evaluation benchmark for assessing the entity disambiguation capabilities of these retrievers, which we call Ambiguous Entity Retrieval (AmbER) sets. |
Anthony Chen; Pallavi Gudipati; Shayne Longpre; Xiao Ling; Sameer Singh; |
| 346 | Evaluation Examples Are Not Equally Informative: How Should That Change NLP Leaderboards? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Building on educational testing, we create a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses. |
Pedro Rodriguez; Joe Barrow; Alexander Miserlis Hoyle; John P. Lalor; Robin Jia; Jordan Boyd-Graber; |
| 347 | Claim Matching Beyond English to Scale Global Fact-Checking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we discuss claim matching as a possible solution to scale fact-checking. |
Ashkan Kazemi; Kiran Garimella; Devin Gaffney; Scott Hale; |
| 348 | SemFace: Pre-training Encoder and Decoder with A Semantic Interface for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a better pre-training method for NMT by defining a semantic interface (SemFace) between the pre-trained encoder and the pre-trained decoder. |
Shuo Ren; Long Zhou; Shujie Liu; Furu Wei; Ming Zhou; Shuai Ma; |
| 349 | Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To benefit from this observation, we train an energy-based model to mimic the behavior of the task measure (i.e., the energy-based model assigns lower energy to samples with higher BLEU score), which is resulted in a re-ranking algorithm based on the samples drawn from NMT: energy-based re-ranking (EBR). |
Sumanta Bhattacharyya; Amirmohammad Rooshenas; Subhajit Naskar; Simeng Sun; Mohit Iyyer; Andrew McCallum; |
| 350 | Syntax-augmented Multilingual BERT for Cross-lingual Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work shows that explicitly providing language syntax and training mBERT using an auxiliary objective to encode the universal dependency tree structure helps cross-lingual transfer. |
Wasi Ahmad; Haoran Li; Kai-Wei Chang; Yashar Mehdad; |
| 351 | How to Adapt Your Pretrained Multilingual Model to 1600 Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we evaluate the performance of existing methods to adapt PMMs to new languages using a resource available for close to 1600 languages: the New Testament. |
Abteen Ebrahimi; Katharina Kann; |
| 352 | Weakly Supervised Named Entity Tagging with Learnable Logical Rules Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel method TALLOR that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner. |
Jiacheng Li; Haibo Ding; Jingbo Shang; Julian McAuley; Zhe Feng; |
| 353 | Prefix-Tuning: Optimizing Continuous Prompts for Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose prefix-tuning, a lightweight alternative to fine-tuning for natural language generation tasks, which keeps language model parameters frozen and instead optimizes a sequence of continuous task-specific vectors, which we call the prefix. |
Xiang Lisa Li; Percy Liang; |
| 354 | One2Set: Generating Diverse Keyphrases As A Set Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new training paradigm One2Set without predefining an order to concatenate the keyphrases. |
Jiacheng Ye; Tao Gui; Yichao Luo; Yige Xu; Qi Zhang; |
| 355 | Continuous Language Generative Flow Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a flow-based language generation model by adapting previous flow generative models to language generation via continuous input embeddings, adapted affine coupling structures, and a novel architecture for autoregressive text generation. |
Zineng Tang; Shiyue Zhang; Hyounghun Kim; Mohit Bansal; |
| 356 | TWAG: A Topic-Guided Wikipedia Abstract Generator Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a two-stage model TWAG that guides the abstract generation with topical information. |
Fangwei Zhu; Shangqing Tu; Jiaxin Shi; Juanzi Li; Lei Hou; Tong Cui; |
| 357 | ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To showcase the usefulness of this task formulation, we introduce ForecastQA, a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts. |
Woojeong Jin; Rahul Khanna; Suji Kim; Dong-Ho Lee; Fred Morstatter; Aram Galstyan; Xiang Ren; |
| 358 | Recursive Tree-Structured Self-Attention for Answer Sentence Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the Tree Aggregation Transformer: a novel recursive, tree-structured self-attention model for AS2. |
Khalil Mrini; Emilia Farcas; Ndapa Nakashole; |
| 359 | How Knowledge Graph and Attention Help? A Qualitative Analysis Into Bag-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE). |
Zikun Hu; Yixin Cao; Lifu Huang; Tat-Seng Chua; |
| 360 | Trigger Is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a Frame-aware Event Argument Extraction (FEAE) learning framework to tackle this issue through reasoning in event frame-level scope. |
Kaiwen Wei; Xian Sun; Zequn Zhang; Jingyuan Zhang; Guo Zhi; Li Jin; |
| 361 | Element Intervention for Open Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit the procedure of OpenRE from a causal view. |
Fangchao Liu; Lingyong Yan; Hongyu Lin; Xianpei Han; Le Sun; |
| 362 | AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present AdaTag, which uses adaptive decoding to handle extraction. |
Jun Yan; Nasser Zalmout; Yan Liang; Christan Grant; Xiang Ren; Xin Luna Dong; |
| 363 | CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a two-stage Collective Relation Integration (CoRI) model, where the first stage independently makes candidate predictions, and the second stage employs a collective model that accesses all candidate predictions to make globally coherent predictions. |
Zhengbao Jiang; Jialong Han; Bunyamin Sisman; Xin Luna Dong; |
| 364 | Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address this issue by compiling a large benchmark adapted from existing free datasets, and performing a comprehensive evaluation of a number of novel and existing baseline models. |
Robert L Logan IV; Andrew McCallum; Sameer Singh; Dan Bikel; |
| 365 | Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. |
Zixuan Li; Xiaolong Jin; Saiping Guan; Wei Li; Jiafeng Guo; Yuanzhuo Wang; Xueqi Cheng; |
| 366 | Employing Argumentation Knowledge Graphs for Neural Argument Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the effectiveness of utilizing knowledge graphs for supporting general text generation tasks, this paper investigates the usage of argumentation-related knowledge graphs to control the generation of arguments. |
Khalid Al Khatib; Lukas Trautner; Henning Wachsmuth; Yufang Hou; Benno Stein; |
| 367 | Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To ease the high computational cost caused by span enumeration, we propose a dual-channel span pruning strategy by incorporating supervision from the Aspect Term Extraction (ATE) and Opinion Term Extraction (OTE) tasks. |
Lu Xu; Yew Ken Chia; Lidong Bing; |
| 368 | On Compositional Generalization of Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study NMT models from the perspective of compositional generalization by building a benchmark dataset, CoGnition, consisting of 216k clean and consistent sentence pairs. |
Yafu Li; Yongjing Yin; Yulong Chen; Yue Zhang; |
| 369 | Mask-Align: Self-Supervised Neural Word Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Mask-Align, a self-supervised word alignment model that takes advantage of the full context on the target side. |
Chi Chen; Maosong Sun; Yang Liu; |
| 370 | GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the task of general word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct the first public benchmark to facilitate research in this topic. |
Huayang Li; Lemao Liu; Guoping Huang; Shuming Shi; |
| 371 | De-biasing Distantly Supervised Named Entity Recognition Via Causal Intervention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we fundamentally explain the dictionary bias via a Structural Causal Model (SCM), categorize the bias into intra-dictionary and inter-dictionary biases, and identify their causes. |
Wenkai Zhang; Hongyu Lin; Xianpei Han; Le Sun; |
| 372 | A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly. |
Fei Li; ZhiChao Lin; Meishan Zhang; Donghong Ji; |
| 373 | MLBiNet: A Cross-Sentence Collective Event Detection Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we reformulate it as a Seq2Seq task and propose a Multi-Layer Bidirectional Network (MLBiNet) to capture the document-level association of events and semantic information simultaneously. |
Dongfang Lou; Zhilin Liao; Shumin Deng; Ningyu Zhang; Huajun Chen; |
| 374 | Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work addresses such limitations by introducing a novel deep learning model for ECR. |
Hieu Minh Tran; Duy Phung; Thien Huu Nguyen; |
| 375 | StereoRel: Relational Triple Extraction from A Stereoscopic Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To intuitively explore the above issues and address them, in this paper, we provide a revealing insight into relational triple extraction from a stereoscopic perspective, which rationalizes the occurrence of these issues and exposes the shortcomings of existing methods. |
Xuetao Tian; Liping Jing; Lu He; Feng Liu; |
| 376 | Knowledge-Enriched Event Causality Identification Via Latent Structure Induction Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by it, we propose a novel Latent Structure Induction Network (LSIN) to incorporate the external structural knowledge into this task. |
Pengfei Cao; Xinyu Zuo; Yubo Chen; Kang Liu; Jun Zhao; Yuguang Chen; Weihua Peng; |
| 377 | Turn The Combination Lock: Learnable Textual Backdoor Attacks Via Word Substitution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present invisible backdoors that are activated by a learnable combination of word substitution. |
Fanchao Qi; Yuan Yao; Sophia Xu; Zhiyuan Liu; Maosong Sun; |
| 378 | Parameter-Efficient Transfer Learning with Diff Pruning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The approach learns a task-specific diff vector that extends the original pretrained parameters. |
Demi Guo; Alexander Rush; Yoon Kim; |
| 379 | R2D2: Recursive Transformer Based on Differentiable Tree for Interpretable Hierarchical Language Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a recursive Transformer model based on differentiable CKY style binary trees to emulate this composition process, and we extend the bidirectional language model pre-training objective to this architecture, attempting to predict each word given its left and right abstraction nodes. |
Xiang Hu; Haitao Mi; Zujie Wen; Yafang Wang; Yi Su; Jing Zheng; Gerard de Melo; |
| 380 | Risk Minimization for Zero-shot Sequence Labeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel unified framework for zero-shot sequence labeling with minimum risk training and design a new decomposable risk function that models the relations between the predicted labels from the source models and the true labels. |
Zechuan Hu; Yong Jiang; Nguyen Bach; Tao Wang; Zhongqiang Huang; Fei Huang; Kewei Tu; |
| 381 | WARP: Word-level Adversarial ReProgramming Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an alternative approach based on adversarial reprogramming, which extends earlier work on automatic prompt generation. |
Karen Hambardzumyan; Hrant Khachatrian; Jonathan May; |
| 382 | Lexicon Learning for Few Shot Sequence Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we augment neural decoders with a lexical translation mechanism that generalizes existing copy mechanisms to incorporate learned, decontextualized, token-level translation rules. |
Ekin Akyurek; Jacob Andreas; |
| 383 | Personalized Transformer for Explainable Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we present a PErsonalized Transformer for Explainable Recommendation (PETER), on which we design a simple and effective learning objective that utilizes the IDs to predict the words in the target explanation, so as to endow the IDs with linguistic meanings and to achieve personalized Transformer. |
Lei Li; Yongfeng Zhang; Li Chen; |
| 384 | Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the first complete pipelines to leverage deep summarization models to generate these notes based on transcripts of conversations between physicians and patients. |
Kundan Krishna; Sopan Khosla; Jeffrey Bigham; Zachary C. Lipton; |
| 385 | Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate the problem of Chinese Grammatical Error Correction (CGEC) and present a new framework named Tail-to-Tail (TtT) non-autoregressive sequence prediction to address the deep issues hidden in CGEC. |
Piji Li; Shuming Shi; |
| 386 | Early Detection of Sexual Predators in Chats Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we instead investigate this problem from the point of view of prevention. |
Matthias Vogt; Ulf Leser; Alan Akbik; |
| 387 | Writing By Memorizing: Hierarchical Retrieval-based Medical Report Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose MedWriter that incorporates a novel hierarchical retrieval mechanism to automatically extract both report and sentence-level templates for clinically accurate report generation. |
Xingyi Yang; Muchao Ye; Quanzeng You; Fenglong Ma; |
| 388 | Concept-Based Label Embedding Via Dynamic Routing for Hierarchical Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel concept-based label embedding method that can explicitly represent the concept and model the sharing mechanism among classes for the hierarchical text classification. |
Xuepeng Wang; Li Zhao; Bing Liu; Tao Chen; Feng Zhang; Di Wang; |
| 389 | VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose VisualSparta, a novel (Visual-text Sparse Transformer Matching) model that shows significant improvement in terms of both accuracy and efficiency. |
Xiaopeng Lu; Tiancheng Zhao; Kyusong Lee; |
| 390 | Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. |
Si Sun; Yingzhuo Qian; Zhenghao Liu; Chenyan Xiong; Kaitao Zhang; Jie Bao; Zhiyuan Liu; Paul Bennett; |
| 391 | Semi-Supervised Text Classification with Balanced Deep Representation Distributions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With this insight, we propose a novel SSTC method, namely Semi-Supervised Text Classification with Balanced Deep representation Distributions (S2TC-BDD). |
Changchun Li; Ximing Li; Jihong Ouyang; |
| 392 | Improving Document Representations By Generating Pseudo Query Embeddings for Dense Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we design a method to mimic the queries to each of the documents by an iterative clustering process and represent the documents by multiple pseudo queries (i.e., the cluster centroids). |
Hongyin Tang; Xingwu Sun; Beihong Jin; Jingang Wang; Fuzheng Zhang; Wei Wu; |
| 393 | ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present ConSERT, a Contrastive Framework for Self-Supervised SEntence Representation Transfer, that adopts contrastive learning to fine-tune BERT in an unsupervised and effective way. |
Yuanmeng Yan; Rumei Li; Sirui Wang; Fuzheng Zhang; Wei Wu; Weiran Xu; |
| 394 | Exploring Dynamic Selection of Branch Expansion Orders for Code Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to equip the Seq2Tree model with a context-based Branch Selector, which is able to dynamically determine optimal expansion orders of branches for multi-branch nodes. |
Hui Jiang; Chulun Zhou; Fandong Meng; Biao Zhang; Jie Zhou; Degen Huang; Qingqiang Wu; Jinsong Su; |
| 395 | COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present Coins, a recursive inference framework that i) iteratively reads context sentences, ii) dynamically generates contextualized inference rules, encodes them, and iii) uses them to guide task-specific output generation. |
Debjit Paul; Anette Frank; |
| 396 | Reasoning Over Entity-Action-Location Graph for Procedural Text Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel approach (REAL) to procedural text understanding, where we build a general framework to systematically model the entity-entity, entity-action, and entity-location relations using a graph neural network. |
Hao Huang; Xiubo Geng; Jian Pei; Guodong Long; Daxin Jiang; |
| 397 | From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing Via Synchronous Semantic Decoding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an unsupervised semantic parsing method – Synchronous Semantic Decoding (SSD), which can simultaneously resolve the semantic gap and the structure gap by jointly leveraging paraphrasing and grammar-constrained decoding. |
Shan Wu; Bo Chen; Chunlei Xin; Xianpei Han; Le Sun; Weipeng Zhang; Jiansong Chen; Fan Yang; Xunliang Cai; |
| 398 | Pre-training Universal Language Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work introduces universal language representation learning, i.e., embeddings of different levels of linguistic units or text with quite diverse lengths in a uniform vector space. |
Yian Li; Hai Zhao; |
| 399 | Structural Pre-training for Dialogue Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present SPIDER, Structural Pre-traIned DialoguE Reader, to capture dialogue exclusive features. |
Zhuosheng Zhang; Hai Zhao; |
| 400 | AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters. |
Yichun Yin; Cheng Chen; Lifeng Shang; Xin Jiang; Xiao Chen; Qun Liu; |
| 401 | Data Augmentation with Adversarial Training for Cross-Lingual NLI Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel data augmentation strategy for better cross-lingual natural language inference by enriching the data to reflect more diversity in a semantically faithful way. |
Xin Dong; Yaxin Zhu; Zuohui Fu; Dongkuan Xu; Gerard de Melo; |
| 402 | Bootstrapped Unsupervised Sentence Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new framework with a two-branch Siamese Network which maximizes the similarity between two augmented views of each sentence. |
Yan Zhang; Ruidan He; Zuozhu Liu; Lidong Bing; Haizhou Li; |
| 403 | Learning Event Graph Knowledge for Abductive Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill this gap, we propose a variational autoencoder based model ege-RoBERTa, which employs a latent variable to capture the necessary commonsense knowledge from event graph for guiding the abductive reasoning task. |
Li Du; Xiao Ding; Ting Liu; Bing Qin; |
| 404 | A Cognitive Regularizer for Language Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore whether the UID hypothesis can be operationalized as an inductive bias for statistical language modeling. |
Jason Wei; Clara Meister; Ryan Cotterell; |
| 405 | Lower Perplexity Is Not Always Human-Like Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages. |
Tatsuki Kuribayashi; Yohei Oseki; Takumi Ito; Ryo Yoshida; Masayuki Asahara; Kentaro Inui; |
| 406 | Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we convert the nearly isolated decisions into interrelated ones by exposing senses in context when learning sense embeddings in a similarity-based Sense Aware Context Exploitation (SACE) architecture. |
Ming Wang; Yinglin Wang; |
| 407 | A Knowledge-Guided Framework for Frame Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. |
Xuefeng Su; Ru Li; Xiaoli Li; Jeff Z. Pan; Hu Zhang; Qinghua Chai; Xiaoqi Han; |
| 408 | Obtaining Better Static Word Embeddings Using Contextual Embedding Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we demonstrate that our proposed distillation method, which is a simple extension of CBOW-based training, allows to significantly improve computational efficiency of NLP applications, while outperforming the quality of existing static embeddings trained from scratch as well as those distilled from previously proposed methods. |
Prakhar Gupta; Martin Jaggi; |
| 409 | Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Aiming to further close this gap, we propose a model of semantic memory for WSD in a meta-learning setting. |
Yingjun Du; Nithin Holla; Xiantong Zhen; Cees Snoek; Ekaterina Shutova; |
| 410 | LexFit: Lexical Fine-Tuning of Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by prior work on semantic specialization of static word embedding (WE) models, we show that it is possible to expose and enrich lexical knowledge from the LMs, that is, to specialize them to serve as effective and universal decontextualized word encoders even when fed input words in isolation (i.e., without any context). |
Ivan Vulic; Edoardo Maria Ponti; Anna Korhonen; Goran Glava?; |
| 411 | Text-Free Image-to-Speech Synthesis Using Learned Segmental Units Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present the first model for directly synthesizing fluent, natural-sounding spoken audio captions for images that does not require natural language text as an intermediate representation or source of supervision. |
Wei-Ning Hsu; David Harwath; Tyler Miller; Christopher Song; James Glass; |
| 412 | CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, the coupled-translation fusion network (CTFN) is firstly proposed to model bi-direction interplay via couple learning, ensuring the robustness in respect to missing modalities. |
Jiajia Tang; Kang Li; Xuanyu Jin; Andrzej Cichocki; Qibin Zhao; Wanzeng Kong; |
| 413 | Positional Artefacts Propagate Through Masked Language Model Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we demonstrate that the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across layers. |
Ziyang Luo; Artur Kulmizev; Xiaoxi Mao; |
| 414 | Language Model Evaluation Beyond Perplexity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an alternate approach to quantifying how well language models learn natural language: we ask how well they match the statistical tendencies of natural language. |
Clara Meister; Ryan Cotterell; |
| 415 | Learning to Explain: Generating Stable Explanations Fast Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Learning to Explain (L2E) approach that learns the behaviour of an underlying explanation algorithm simultaneously from all training examples. |
Xuelin Situ; Ingrid Zukerman; Cecile Paris; Sameen Maruf; Gholamreza Haffari; |
| 416 | StereoSet: Measuring Stereotypical Bias in Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present StereoSet, a large-scale natural English dataset to measure stereotypical biases in four domains: gender, profession, race, and religion. |
Moin Nadeem; Anna Bethke; Siva Reddy; |
| 417 | Alignment Rationale for Natural Language Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, this paper presents AREC, a post-hoc approach to generate alignment rationale explanations for co-attention based models in NLI. |
Zhongtao Jiang; Yuanzhe Zhang; Zhao Yang; Jun Zhao; Kang Liu; |
| 418 | Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression Based on Matrix Product Operators Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics. |
Peiyu Liu; Ze-Feng Gao; Wayne Xin Zhao; Zhi-Yuan Xie; Zhong-Yi Lu; Ji-Rong Wen; |
| 419 | On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, for the first time, we can improve the interpretability of explanations by allowing arbitrary text sequences as the explanation unit. |
Wei Zhang; Ziming Huang; Yada Zhu; Guangnan Ye; Xiaodong Cui; Fan Zhang; |
| 420 | Syntax-Enhanced Pre-trained Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. |
Zenan Xu; Daya Guo; Duyu Tang; Qinliang Su; Linjun Shou; Ming Gong; Wanjun Zhong; Xiaojun Quan; Daxin Jiang; Nan Duan; |
| 421 | Matching Distributions Between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a generic framework named Cross-domain Knowledge Distillation (CdKD) without needing any source data. |
Bo Zhang; Xiaoming Zhang; Yun Liu; Lei Cheng; Zhoujun Li; |
| 422 | Counterfactual Inference for Text Classification Debiasing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by this, we propose a model-agnostic text classification debiasing framework – Corsair, which can effectively avoid employing data manipulations or designing balancing mechanisms. |
Chen Qian; Fuli Feng; Lijie Wen; Chunping Ma; Pengjun Xie; |
| 423 | HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a news recommendation method with hierarchical user interest modeling, named HieRec. |
Tao Qi; Fangzhao Wu; Chuhan Wu; Peiru Yang; Yang Yu; Xing Xie; Yongfeng Huang; |
| 424 | PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, in this paper we propose to incorporate news popularity information to alleviate the cold-start and diversity problems for personalized news recommendation. |
Tao Qi; Fangzhao Wu; Chuhan Wu; Yongfeng Huang; |
| 425 | Article Reranking By Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel reranker, MTM (Memory-enhanced Transformers for Matching) to rank FC-articles using key sentences selected with event (lexical and semantic) and pattern information. |
Qiang Sheng; Juan Cao; Xueyao Zhang; Xirong Li; Lei Zhong; |
| 426 | Defense Against Synonym Substitution-based Adversarial Attacks Via Dirichlet Neighborhood Ensemble Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Dirichlet Neighborhood Ensemble (DNE), a randomized method for training a robust model to defense synonym substitution-based attacks. |
Yi Zhou; Xiaoqing Zheng; Cho-Jui Hsieh; Kai-Wei Chang; Xuanjing Huang; |
| 427 | Shortformer: Better Language Modeling Using Shorter Inputs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We identify conditions where shorter inputs are not harmful, and achieve perplexity and efficiency improvements through two new methods that decrease input length. |
Ofir Press; Noah A. Smith; Mike Lewis; |
| 428 | BanditMTL: Bandit-based Multi-task Learning for Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, to fill this gap, this paper investigates how the task might be effectively regularized, and consequently proposes a multi-task learning method based on adversarial multi-armed bandit. |
Yuren Mao; Zekai Wang; Weiwei Liu; Xuemin Lin; Wenbin Hu; |
| 429 | Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We attempted to solve this problem by using the Bregman divergence to provide a unified interpretation of the softmax cross-entropy and negative sampling loss functions. |
Hidetaka Kamigaito; Katsuhiko Hayashi; |
| 430 | De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a de-confounded variational encoder-decoder (DCVED) based on causal intervention, learning the objective p(\boldsymbol{y}|\text{do}(\boldsymbol{x})). |
Wenqing Chen; Jidong Tian; Yitian Li; Hao He; Yaohui Jin; |
| 431 | Rethinking Stealthiness of Backdoor Attack Against NLP Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we point out a potential problem of current backdoor attacking research: its evaluation ignores the stealthiness of backdoor attacks, and most of existing backdoor attacking methods are not stealthy either to system deployers or to system users. |
Wenkai Yang; Yankai Lin; Peng Li; Jie Zhou; Xu Sun; |
| 432 | Crowdsourcing Learning As Domain Adaptation: A Case Study on Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We take a different point in this work, regarding all crowdsourced annotations as gold-standard with respect to the individual annotators. |
Xin Zhang; Guangwei Xu; Yueheng Sun; Meishan Zhang; Pengjun Xie; |
| 433 | Exploring Distantly-Labeled Rationales in Neural Network Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose two novel auxiliary loss functions to make better use of distantly-labeled rationales, which encourage models to maintain their focus on important words beyond labeled rationales (PINs) and alleviate redundant training on non-helpful rationales (NoIRs). |
Quzhe Huang; Shengqi Zhu; Yansong Feng; Dongyan Zhao; |
| 434 | Learning to Perturb Word Embeddings for Out-of-distribution QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose a simple yet effective DA method based on a stochastic noise generator, which learns to perturb the word embedding of the input questions and context without changing their semantics. |
Seanie Lee; Minki Kang; Juho Lee; Sung Ju Hwang; |
| 435 | Maria: A Visual Experience Powered Conversational Agent Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take a step further to study image-grounded conversation under a fully open-ended setting where no paired dialog and image are assumed available. |
Zujie Liang; Huang Hu; Can Xu; Chongyang Tao; Xiubo Geng; Yining Chen; Fan Liang; Daxin Jiang; |
| 436 | A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a human-machine collaborative framework, HMCEval, that can guarantee reliability of the evaluation outcomes with reduced human effort. |
Yangjun Zhang; Pengjie Ren; Maarten de Rijke; |
| 437 | Generating Relevant and Coherent Dialogue Responses Using Self-Separated Conditional Variational AutoEncoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To resolve this problem, we propose Self-separated Conditional Variational AutoEncoder (abbreviated as SepaCVAE) that introduces group information to regularize the latent variables, which enhances CVAE by improving the responses’ relevance and coherence while maintaining their diversity and informativeness. |
Bin Sun; Shaoxiong Feng; Yiwei Li; Jiamou Liu; Kan Li; |
| 438 | Learning to Ask Conversational Questions By Optimizing Levenshtein Distance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimizes the minimum Levenshtein distance through explicit editing actions. |
Zhongkun Liu; Pengjie Ren; Zhumin Chen; Zhaochun Ren; Maarten de Rijke; Ming Zhou; |
| 439 | DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these limitations, in this paper, we present DVD, a Diagnostic Dataset for Video-grounded Dialogue. |
Hung Le; Chinnadhurai Sankar; Seungwhan Moon; Ahmad Beirami; Alborz Geramifard; Satwik Kottur; |
| 440 | MMGCN: Multimodal Fusion Via Deep Graph Convolution Network for Emotion Recognition in Conversation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to explore a more effective way of utilizing both multimodal and long-distance contextual information, we propose a new model based on multimodal fused graph convolutional network, MMGCN, in this work. |
Jingwen Hu; Yuchen Liu; Jinming Zhao; Qin Jin; |
| 441 | DynaEval: Unifying Turn and Dialogue Level Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose DynaEval, a unified automatic evaluation framework which is not only capable of performing turn-level evaluation, but also holistically considers the quality of the entire dialogue. |
Chen Zhang; Yiming Chen; Luis Fernando D?Haro; Yan Zhang; Thomas Friedrichs; Grandee Lee; Haizhou Li; |
| 442 | CoSQA: 20,000+ Web Queries for Code Search and Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To remedy this, we introduce CoSQA dataset. It includes 20,604 labels for pairs of natural language queries and codes, each annotated by at least 3 human annotators. |
Junjie Huang; Duyu Tang; Linjun Shou; Ming Gong; Ke Xu; Daxin Jiang; Ming Zhou; Nan Duan; |
| 443 | Rewriter-Evaluator Architecture for Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we introduce a novel architecture of Rewriter-Evaluator. |
Yangming Li; Kaisheng Yao; |
| 444 | Modeling Bilingual Conversational Characteristics for Neural Chat Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to promote the translation quality of conversational text by modeling the above properties. |
Yunlong Liang; Fandong Meng; Yufeng Chen; Jinan Xu; Jie Zhou; |
| 445 | Importance-based Neuron Allocation for Multilingual Neural Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve these problems, we propose to divide the model neurons into general and language-specific parts based on their importance across languages. |
Wanying Xie; Yang Feng; Shuhao Gu; Dong Yu; |
| 446 | Transfer Learning for Sequence Generation: from Single-source to Multi-source Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose a two-stage finetuning method to alleviate the pretrain-finetune discrepancy and introduce a novel MSG model with a fine encoder to learn better representations in MSG tasks. |
Xuancheng Huang; Jingfang Xu; Maosong Sun; Yang Liu; |
| 447 | A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model exhibits a high degree of sensitivity to the selection of few shots. |
Mengjie Zhao; Yi Zhu; Ehsan Shareghi; Ivan Vulic; Roi Reichart; Anna Korhonen; Hinrich Sch?tze; |
| 448 | Coreference Reasoning in Machine Reading Comprehension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a methodology for creating MRC datasets that better reflect the challenges of coreference reasoning and use it to create a sample evaluation set. |
Mingzhu Wu; Nafise Sadat Moosavi; Dan Roth; Iryna Gurevych; |
| 449 | Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple yet effective method to adapt unsupervised syntactic dependency parsing methodology for unsupervised discourse dependency parsing. |
Liwen Zhang; Ge Wang; Wenjuan Han; Kewei Tu; |
| 450 | A Conditional Splitting Framework for Efficient Constituency Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a generic seq2seq parsing framework that casts constituency parsing problems (syntactic and discourse parsing) into a series of conditional splitting decisions. |
Thanh-Tung Nguyen; Xuan-Phi Nguyen; Shafiq Joty; Xiaoli Li; |
| 451 | A Unified Generative Framework for Various NER Subtasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework. |
Hang Yan; Tao Gui; Junqi Dai; Qipeng Guo; Zheng Zhang; Xipeng Qiu; |
| 452 | An In-depth Study on Internal Structure of Chinese Words Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work proposes to model the deep internal structures of Chinese words as dependency trees with 11 labels for distinguishing syntactic relationships. |
Chen Gong; Saihao Huang; Houquan Zhou; Zhenghua Li; Min Zhang; Zhefeng Wang; Baoxing Huai; Nicholas Jing Yuan; |
| 453 | MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper addresses zero-shot transfer for cross-lingual NER, especially when the amount of source-language training data is also limited. |
Linlin Liu; Bosheng Ding; Lidong Bing; Shafiq Joty; Luo Si; Chunyan Miao; |
| 454 | Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Lexicon Enhanced BERT (LEBERT) for Chinese sequence labeling, which integrates external lexicon knowledge into BERT layers directly by a Lexicon Adapter layer. |
Wei Liu; Xiyan Fu; Yue Zhang; Wenming Xiao; |
| 455 | Math Word Problem Solving with Explicit Numerical Values Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel approach called NumS2T, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network. |
Qinzhuo Wu; Qi Zhang; Zhongyu Wei; Xuanjing Huang; |
| 456 | Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Herein, we propose Neural-Symbolic Solver (NS-Solver) to explicitly and seamlessly incorporate different levels of symbolic constraints by auxiliary tasks. |
Jinghui Qin; Xiaodan Liang; Yining Hong; Jianheng Tang; Liang Lin; |
| 457 | SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantic knowledge from neighbours of linked-entity. |
Taolin Zhang; Zerui Cai; Chengyu Wang; Minghui Qiu; Bite Yang; Xiaofeng He; |
| 458 | What Is Your Article Based On? Inferring Fine-grained Provenance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce new techniques to model and reason about the provenance of multiple interacting claims, including how to capture fine-grained information about the context. |
Yi Zhang; Zachary Ives; Dan Roth; |
| 459 | Cross-modal Memory Networks for Radiology Report Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a cross-modal memory networks (CMN) to enhance the encoder-decoder framework for radiology report generation, where a shared memory is designed to record the alignment between images and texts so as to facilitate the interaction and generation across modalities. |
Zhihong Chen; Yaling Shen; Yan Song; Xiang Wan; |
| 460 | Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose novel personalized approaches that respect individual beliefs expressed by either user conformity-based measures or various embeddings of their previous text annotations. |
Kamil Kanclerz; Alicja Figas; Marcin Gruza; Tomasz Kajdanowicz; Jan Kocon; Daria Puchalska; Przemyslaw Kazienko; |
| 461 | Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new task Multimodal Review Helpfulness Prediction (MRHP) aiming to analyze the review helpfulness from text and visual modalities. |
Junhao Liu; Zhen Hai; Min Yang; Lidong Bing; |
| 462 | Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Shallow Aggressive Decoding (SAD) to improve the online inference efficiency of the Transformer for instantaneous Grammatical Error Correction (GEC). |
Xin Sun; Tao Ge; Furu Wei; Houfeng Wang; |
| 463 | Automatic ICD Coding Via Interactive Shared Representation Networks with Self-distillation Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above issues, we propose an Interactive Shared Representation Network with Self-Distillation Mechanism. |
Tong Zhou; Pengfei Cao; Yubo Chen; Kang Liu; Jun Zhao; Kun Niu; Weifeng Chong; Shengping Liu; |
| 464 | PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above issues, we propose a novel end-to-end trainable model called PHMOSpell, which promotes the performance of CSC with multi-modal information. |
Li Huang; Junjie Li; Weiwei Jiang; Zhiyu Zhang; Minchuan Chen; Shaojun Wang; Jing Xiao; |
| 465 | Guiding The Growth: Difficulty-Controllable Question Generation Through Step-by-Step Rewriting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. |
Yi Cheng; Siyao Li; Bang Liu; Ruihui Zhao; Sujian Li; Chenghua Lin; Yefeng Zheng; |
| 466 | Improving Encoder By Auxiliary Supervision Tasks for Table-to-Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Consequently, we propose to utilize two auxiliary tasks, Number Ranking (NR) and Importance Ranking (IR), to supervise the encoder to capture the different relations. |
Liang Li; Can Ma; Yinliang Yue; Dayong Hu; |
| 467 | POS-Constrained Parallel Decoding for Non-autoregressive Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To provide a feasible solution to the multimodality problem of NAG, we propose incorporating linguistic structure (Part-of-Speech sequence in particular) into NAG inference instead of relying on teacher AG. |
Kexin Yang; Wenqiang Lei; Dayiheng Liu; Weizhen Qi; Jiancheng Lv; |
| 468 | Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Towards approaching this problem, we extend the vanilla pretrain-finetune pipeline with an extra embedding transfer step. |
Xin Liu; Baosong Yang; Dayiheng Liu; Haibo Zhang; Weihua Luo; Min Zhang; Haiying Zhang; Jinsong Su; |
| 469 | TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to deeply understand the capability of pretrained language models in text generation and conduct a diagnostic evaluation, we propose TGEA, an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models (PLMs). |
Jie He; Bo Peng; Yi Liao; Qun Liu; Deyi Xiong; |
| 470 | Long-Span Summarization Via Local Attention and Content Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we exploit large pre-trained transformer-based models and address long-span dependencies in abstractive summarization using two methods: local self-attention; and explicit content selection. |
Potsawee Manakul; Mark Gales; |
| 471 | RepSum: Unsupervised Dialogue Summarization Based on Replacement Strategy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we propose a novel unsupervised strategy to address this challenge, which roots from the hypothetical foundation that a superior summary approximates a replacement of the original dialogue, and they are roughly equivalent for auxiliary (self-supervised) tasks, e.g., dialogue generation. |
Xiyan Fu; Yating Zhang; Tianyi Wang; Xiaozhong Liu; Changlong Sun; Zhenglu Yang; |
| 472 | BASS: Boosting Abstractive Summarization with Unified Semantic Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present BASS, a novel framework for Boosting Abstractive Summarization based on a unified Semantic graph, which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases. |
Wenhao Wu; Wei Li; Xinyan Xiao; Jiachen Liu; Ziqiang Cao; Sujian Li; Hua Wu; Haifeng Wang; |
| 473 | Capturing Relations Between Scientific Papers: An Abstractive Model for Related Work Section Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area. |
Xiuying Chen; Hind Alamro; Mingzhe Li; Shen Gao; Xiangliang Zhang; Dongyan Zhao; Rui Yan; |
| 474 | Focus Attention: Promoting Faithfulness and Diversity in Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With the motivation to narrow this gap, we introduce Focus Attention Mechanism, a simple yet effective method to encourage decoders to proactively generate tokens that are similar or topical to the input document. |
Rahul Aralikatte; Shashi Narayan; Joshua Maynez; Sascha Rothe; Ryan McDonald; |
| 475 | Generating Query Focused Summaries from Query-Free Resources Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce MaRGE, a Masked ROUGE Regression framework for evidence estimation and ranking which relies on a unified representation for summaries and queries, so that summaries in generic data can be converted into proxy queries for learning a query model. |
Yumo Xu; Mirella Lapata; |
| 476 | Robustifying Multi-hop QA Through Pseudo-Evidentiality Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In contrast, we propose a new approach to learn evidentiality, deciding whether the answer prediction is supported by correct evidences, without such annotations. |
Kyungjae Lee; Seung-won Hwang; Sang-eun Han; Dohyeon Lee; |
| 477 | XMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new contrastive learning method called Cross Momentum Contrastive learning (xMoCo), for learning a dual-encoder model for question-passage matching. |
Nan Yang; Furu Wei; Binxing Jiao; Daxing Jiang; Linjun Yang; |
| 478 | Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework, ExCorD (Explicit guidance on how to resolve Conversational Dependency) to enhance the abilities of QA models in comprehending conversational context. |
Gangwoo Kim; Hyunjae Kim; Jungsoo Park; Jaewoo Kang; |
| 479 | PhotoChat: A Human-Human Dialogue Dataset With Photo Sharing Behavior For Joint Image-Text Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new human-human dialogue dataset – PhotoChat, the first dataset that casts light on the photo sharing behavior in online messaging. |
Xiaoxue Zang; Lijuan Liu; Maria Wang; Yang Song; Hao Zhang; Jindong Chen; |
| 480 | Good for Misconceived Reasons: An Empirical Revisiting on The Need for Visual Context in Multimodal Machine Translation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Upon further investigation, we discover that the improvements achieved by the multimodal models over text-only counterparts are in fact results of the regularization effect. |
Zhiyong Wu; Lingpeng Kong; Wei Bi; Xiang Li; Ben Kao; |
| 481 | Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Motion-Appearance Synergistic Networks (MASN), which embed two cross-modal features grounded on motion and appearance information and selectively utilize them depending on the question’s intentions. |
Ahjeong Seo; Gi-Cheon Kang; Joonhan Park; Byoung-Tak Zhang; |
| 482 | BERTifying The Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way. |
Yinghao Li; Pranav Shetty; Lucas Liu; Chao Zhang; Le Song; |
| 483 | CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we go beyond typical MIL framework and propose a novel contrastive instance learning (CIL) framework. |
Tao Chen; Haizhou Shi; Siliang Tang; Zhigang Chen; Fei Wu; Yueting Zhuang; |
| 484 | SENT: Sentence-level Distant Relation Extraction Via Negative Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on NT, we propose a sentence-level framework, SENT, for distant relation extraction. |
Ruotian Ma; Tao Gui; Linyang Li; Qi Zhang; Xuanjing Huang; Yaqian Zhou; |
| 485 | An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To avoid the disadvantages of existing models and exploit the generalized representation across the two tasks, we design an end-to-end progressive multi-task learning model for jointly modeling medical NER and NEN in an effective way. |
Baohang Zhou; Xiangrui Cai; Ying Zhang; Xiaojie Yuan; |
| 486 | PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). |
Hengyi Zheng; Rui Wen; Xi Chen; Yifan Yang; Yunyan Zhang; Ziheng Zhang; Ningyu Zhang; Bin Qin; Xu Ming; Yefeng Zheng; |
| 487 | Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the issue, we propose a novel model, Mining Undefined Classes from Other-class (MUCO), that can automatically induce different unde- fined classes from the other class to improve few-shot NER. |
Meihan Tong; Shuai Wang; Bin Xu; Yixin Cao; Minghui Liu; Lei Hou; Juanzi Li; |
| 488 | Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by how humans look up relevant information to comprehend a scientific text, we present a novel framework that utilizes external knowledge for joint entity and relation extraction named KECI (Knowledge-Enhanced Collective Inference). |
Tuan Lai; Heng Ji; ChengXiang Zhai; Quan Hung Tran; |
| 489 | Fine-grained Information Extraction from Biomedical Literature Based on Knowledge-enriched Abstract Meaning Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel biomedical Information Extraction (IE) model to tackle these two challenges and extract scientific entities and events from English research papers. |
Zixuan Zhang; Nikolaus Parulian; Heng Ji; Ahmed Elsayed; Skatje Myers; Martha Palmer; |
| 490 | Unleash GPT-2 Power for Event Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this issue, we propose to exploit the powerful pre-trained language model GPT-2 to generate training samples for ED. |
Amir Pouran Ben Veyseh; Viet Lai; Franck Dernoncourt; Thien Huu Nguyen; |
| 491 | CLEVE: Contrastive Pre-training for Event Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose CLEVE, a contrastive pre-training framework for EE to better learn event knowledge from large unsupervised data and their semantic structures (e.g. AMR) obtained with automatic parsers. |
Ziqi Wang; Xiaozhi Wang; Xu Han; Yankai Lin; Lei Hou; Zhiyuan Liu; Peng Li; Juanzi Li; Jie Zhou; |
| 492 | Document-level Event Extraction Via Parallel Prediction Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an end-to-end model, which can extract structured events from a document in a parallel manner. |
Hang Yang; Dianbo Sui; Yubo Chen; Kang Liu; Jun Zhao; Taifeng Wang; |
| 493 | StructuralLM: Structural Pre-training for Form Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new pre-training approach, StructuralLM, to jointly leverage cell and layout information from scanned documents. |
Chenliang Li; Bin Bi; Ming Yan; Wei Wang; Songfang Huang; Fei Huang; Luo Si; |
| 494 | Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these challenges, in this paper, we propose a dual graph convolutional networks (DualGCN) model that considers the complementarity of syntax structures and semantic correlations simultaneously. |
Ruifan Li; Hao Chen; Fangxiang Feng; Zhanyu Ma; Xiaojie Wang; Eduard Hovy; |
| 495 | Multi-Label Few-Shot Learning for Aspect Category Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formulate ACD in the few-shot learning scenario. |
Mengting Hu; Shiwan Zhao; Honglei Guo; Chao Xue; Hang Gao; Tiegang Gao; Renhong Cheng; Zhong Su; |
| 496 | Argument Pair Extraction Via Attention-guided Multi-Layer Multi-Cross Encoding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel attention-guided multi-layer multi-cross encoding scheme to address the challenges. |
Liying Cheng; Tianyu Wu; Lidong Bing; Luo Si; |
| 497 | A Neural Transition-based Model for Argumentation Mining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Towards these issues, we propose a neural transition-based model for argumentation mining, which incrementally builds an argumentation graph by generating a sequence of actions, avoiding inefficient enumeration operations. |
Jianzhu Bao; Chuang Fan; Jipeng Wu; Yixue Dang; Jiachen Du; Ruifeng Xu; |
| 498 | Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. |
Philippe Laban; Tobias Schnabel; Paul Bennett; Marti A. Hearst; |
| 499 | Long Text Generation By Modeling Sentence-Level and Discourse-Level Coherence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a long text generation model, which can represent the prefix sentences at sentence level and discourse level in the decoding process. |
Jian Guan; Xiaoxi Mao; Changjie Fan; Zitao Liu; Wenbiao Ding; Minlie Huang; |
| 500 | OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: OpenMEVA provides a comprehensive test suite to assess the capabilities of metrics, including (a) the correlation with human judgments, (b) the generalization to different model outputs and datasets, (c) the ability to judge story coherence, and (d) the robustness to perturbations |
Jian Guan; Zhexin Zhang; Zhuoer Feng; Zitao Liu; Wenbiao Ding; Xiaoxi Mao; Changjie Fan; Minlie Huang; |
| 501 | DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose DYPLOC, a generation framework that conducts dynamic planning of content while generating the output based on a novel design of mixed language models. |
Xinyu Hua; Ashwin Sreevatsa; Lu Wang; |
| 502 | Controllable Open-ended Question Generation with A New Question Type Ontology Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate the less-explored task of generating open-ended questions that are typically answered by multiple sentences. |
Shuyang Cao; Lu Wang; |
| 503 | BERTGen: Multi-task Generation Through BERT |