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1, TITLE: Detecting Attackable Sentences in Arguments https://www.aclweb.org/anthology/2020.emnlp-main.1 AUTHORS: Yohan Jo, Seojin Bang, Emaad Manzoor, Eduard Hovy, Chris Reed HIGHLIGHT: We present a first large-scale analysis of sentence attackability in online arguments.
2, TITLE: Extracting Implicitly Asserted Propositions in Argumentation https://www.aclweb.org/anthology/2020.emnlp-main.2 AUTHORS: Yohan Jo, Jacky Visser, Chris Reed, Eduard Hovy HIGHLIGHT: In this paper, we examine a wide range of computational methods for extracting propositions that are implicitly asserted in questions, reported speech, and imperatives in argumentation.
3, TITLE: Quantitative argument summarization and beyond: Cross-domain key point analysis https://www.aclweb.org/anthology/2020.emnlp-main.3 AUTHORS: Roy Bar-Haim, Yoav Kantor, Lilach Eden, Roni Friedman, Dan Lahav, Noam Slonim HIGHLIGHT: The current work advances key point analysis in two important respects: first, we develop a method for automatic extraction of key points, which enables fully automatic analysis, and is shown to achieve performance comparable to a human expert. Second, we demonstrate that the applicability of key point analysis goes well beyond argumentation data.
4, TITLE: Unsupervised stance detection for arguments from consequences https://www.aclweb.org/anthology/2020.emnlp-main.4 AUTHORS: Jonathan Kobbe, Ioana Hulpu?, Heiner Stuckenschmidt HIGHLIGHT: In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic.
5, TITLE: BLEU might be Guilty but References are not Innocent https://www.aclweb.org/anthology/2020.emnlp-main.5 AUTHORS: Markus Freitag, David Grangier, Isaac Caswell HIGHLIGHT: We study different methods to collect references and compare their value in automated evaluation by reporting correlation with human evaluation for a variety of systems and metrics.
6, TITLE: Statistical Power and Translationese in Machine Translation Evaluation https://www.aclweb.org/anthology/2020.emnlp-main.6 AUTHORS: Yvette Graham, Barry Haddow, Philipp Koehn HIGHLIGHT: The term translationese has been used to describe features of translated text, and in this paper, we provide detailed analysis of potential adverse effects of translationese on machine translation evaluation.
7, TITLE: Simulated multiple reference training improves low-resource machine translation https://www.aclweb.org/anthology/2020.emnlp-main.7 AUTHORS: Huda Khayrallah, Brian Thompson, Matt Post, Philipp Koehn HIGHLIGHT: We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser's distribution over possible tokens.
8, TITLE: Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing https://www.aclweb.org/anthology/2020.emnlp-main.8 AUTHORS: Brian Thompson, Matt Post HIGHLIGHT: We propose training the paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot translation task (e.g., Czech to Czech).
9, TITLE: PRover: Proof Generation for Interpretable Reasoning over Rules https://www.aclweb.org/anthology/2020.emnlp-main.9 AUTHORS: Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, Mohit Bansal HIGHLIGHT: In our work, we take a step closer to emulating formal theorem provers, by proposing PRover, an interpretable transformer-based model that jointly answers binary questions over rule-bases and generates the corresponding proofs.
10, TITLE: Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question- Answering https://www.aclweb.org/anthology/2020.emnlp-main.10 AUTHORS: Harsh Jhamtani, Peter Clark HIGHLIGHT: To address this, we introduce three explanation datasets in which explanations formed from corpus facts are annotated.
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11, TITLE: Self-Supervised Knowledge Triplet Learning for Zero-Shot Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.11 AUTHORS: Pratyay Banerjee, Chitta Baral HIGHLIGHT: This work proposes Knowledge Triplet Learning (KTL), a self-supervised task over knowledge graphs.
12, TITLE: More Bang for Your Buck: Natural Perturbation for Robust Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.12 AUTHORS: Daniel Khashabi, Tushar Khot, Ashish Sabharwal HIGHLIGHT: As an alternative to the traditional approach of creating new instances by repeating the process of creating one instance, we propose doing so by first collecting a set of seed examples and then applying human-driven natural perturbations (as opposed to rule-based machine perturbations), which often change the gold label as well.
13, TITLE: A matter of framing: The impact of linguistic formalism on probing results https://www.aclweb.org/anthology/2020.emnlp-main.13 AUTHORS: Ilia Kuznetsov, Iryna Gurevych HIGHLIGHT: To investigate, we conduct an in-depth cross-formalism layer probing study in role semantics.
14, TITLE: Information-Theoretic Probing with Minimum Description Length https://www.aclweb.org/anthology/2020.emnlp-main.14 AUTHORS: Elena Voita, Ivan Titov HIGHLIGHT: Instead, we propose an alternative to the standard probes, information-theoretic probing with minimum description length (MDL).
15, TITLE: Intrinsic Probing through Dimension Selection https://www.aclweb.org/anthology/2020.emnlp-main.15 AUTHORS: Lucas Torroba Hennigen, Adina Williams, Ryan Cotterell HIGHLIGHT: To enable intrinsic probing, we propose a novel framework based on a decomposable multivariate Gaussian probe that allows us to determine whether the linguistic information in word embeddings is dispersed or focal.
16, TITLE: Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually) https://www.aclweb.org/anthology/2020.emnlp-main.16 AUTHORS: Alex Warstadt, Yian Zhang, Xiaocheng Li, Haokun Liu, Samuel R. Bowman HIGHLIGHT: With this goal in mind, we introduce a new English-language diagnostic set called MSGS (the Mixed Signals Generalization Set), which consists of 20 ambiguous binary classification tasks that we use to test whether a pretrained model prefers linguistic or surface generalizations during finetuning.
17, TITLE: Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference https://www.aclweb.org/anthology/2020.emnlp-main.17 AUTHORS: Bang An, Jie Lyu, Zhenyi Wang, Chunyuan Li, Changwei Hu, Fei Tan, Ruiyi Zhang, Yifan Hu, Changyou Chen HIGHLIGHT: In this paper, for the first time, we provide a novel understanding of multi-head attention from a Bayesian perspective.
18, TITLE: KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations https://www.aclweb.org/anthology/2020.emnlp-main.18 AUTHORS: Fabio Massimo Zanzotto, Andrea Santilli, Leonardo Ranaldi, Dario Onorati, Pierfrancesco Tommasino, Francesca Fallucchi HIGHLIGHT: In this paper, we propose KERMIT (Kernel-inspired Encoder with Recursive Mechanism for Interpretable Trees) to embed symbolic syntactic parse trees into artificial neural networks and to visualize how syntax is used in inference.
19, TITLE: ETC: Encoding Long and Structured Inputs in Transformers https://www.aclweb.org/anthology/2020.emnlp-main.19 AUTHORS: Joshua Ainslie, Santiago Ontanon, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, Li Yang HIGHLIGHT: In this paper, we present a new Transformer architecture, "Extended Transformer Construction" (ETC), that addresses two key challenges of standard Transformer architectures, namely scaling input length and encoding structured inputs.
20, TITLE: Pre-Training Transformers as Energy-Based Cloze Models https://www.aclweb.org/anthology/2020.emnlp-main.20
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AUTHORS: Kevin Clark, Minh-Thang Luong, Quoc Le, Christopher D. Manning HIGHLIGHT: We introduce Electric, an energy-based cloze model for representation learning over text.
21, TITLE: Calibration of Pre-trained Transformers https://www.aclweb.org/anthology/2020.emnlp-main.21 AUTHORS: Shrey Desai, Greg Durrett HIGHLIGHT: We focus on BERT and RoBERTa in this work, and analyze their calibration across three tasks: natural language inference, paraphrase detection, and commonsense reasoning.
22, TITLE: Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding https://www.aclweb.org/anthology/2020.emnlp-main.22 AUTHORS: Jiaming Shen, Heng Ji, Jiawei Han HIGHLIGHT: In this study, we present a new linguistic steganography method which encodes secret messages using self- adjusting arithmetic coding based on a neural language model.
23, TITLE: Multi-Dimensional Gender Bias Classification https://www.aclweb.org/anthology/2020.emnlp-main.23 AUTHORS: Emily Dinan, Angela Fan, Ledell Wu, Jason Weston, Douwe Kiela, Adina Williams HIGHLIGHT: In this work, we propose a novel, general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
24, TITLE: FIND: Human-in-the-Loop Debugging Deep Text Classifiers https://www.aclweb.org/anthology/2020.emnlp-main.24 AUTHORS: Piyawat Lertvittayakumjorn, Lucia Specia, Francesca Toni HIGHLIGHT: In this paper, we propose FIND - a framework which enables humans to debug deep learning text classifiers by disabling irrelevant hidden features.
25, TITLE: Conversational Document Prediction to Assist Customer Care Agents https://www.aclweb.org/anthology/2020.emnlp-main.25 AUTHORS: Jatin Ganhotra, Haggai Roitman, Doron Cohen, Nathaniel Mills, Chulaka Gunasekara, Yosi Mass, Sachindra Joshi, Luis Lastras, David Konopnicki HIGHLIGHT: We study the task of predicting the documents that customer care agents can use to facilitate users' needs.
26, TITLE: Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLU https://www.aclweb.org/anthology/2020.emnlp-main.26 AUTHORS: Brielen Madureira, David Schlangen HIGHLIGHT: We investigate how they behave under incremental interfaces, when partial output must be provided based on partial input seen up to a certain time step, which may happen in interactive systems.
27, TITLE: Augmented Natural Language for Generative Sequence Labeling https://www.aclweb.org/anthology/2020.emnlp-main.27 AUTHORS: Ben Athiwaratkun, Cicero Nogueira dos Santos, Jason Krone, Bing Xiang HIGHLIGHT: We propose a generative framework for joint sequence labeling and sentence-level classification.
28, TITLE: Dialogue Response Ranking Training with Large-Scale Human Feedback Data https://www.aclweb.org/anthology/2020.emnlp-main.28 AUTHORS: Xiang Gao, Yizhe Zhang, Michel Galley, Chris Brockett, Bill Dolan HIGHLIGHT: We leverage social media feedback data (number of replies and upvotes) to build a large-scale training dataset for feedback prediction.
29, TITLE: Semantic Evaluation for Text-to-SQL with Distilled Test Suites https://www.aclweb.org/anthology/2020.emnlp-main.29 AUTHORS: Ruiqi Zhong, Tao Yu, Dan Klein HIGHLIGHT: We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models.
30, TITLE: Cross-Thought for Sentence Encoder Pre-training https://www.aclweb.org/anthology/2020.emnlp-main.30 AUTHORS: Shuohang Wang, Yuwei Fang, Siqi Sun, Zhe Gan, Yu Cheng, Jingjing Liu, Jing Jiang
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HIGHLIGHT: In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering.
31, TITLE: AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data https://www.aclweb.org/anthology/2020.emnlp-main.31 AUTHORS: Silei Xu, Sina Semnani, Giovanni Campagna, Monica Lam HIGHLIGHT: We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort.
32, TITLE: A Spectral Method for Unsupervised Multi-Document Summarization https://www.aclweb.org/anthology/2020.emnlp-main.32 AUTHORS: Kexiang Wang, Baobao Chang, Zhifang Sui HIGHLIGHT: In this paper, we propose a spectral-based hypothesis, which states that the goodness of summary candidate is closely linked to its so-called spectral impact.
33, TITLE: What Have We Achieved on Text Summarization? https://www.aclweb.org/anthology/2020.emnlp-main.33 AUTHORS: Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, Yue Zhang HIGHLIGHT: Aiming to gain more understanding of summarization systems with respect to their strengths and limits on a fine-grained syntactic and semantic level, we consult the Multidimensional Quality Metric (MQM) and quantify 8 major sources of errors on 10 representative summarization models manually.
34, TITLE: Q-learning with Language Model for Edit-based Unsupervised Summarization https://www.aclweb.org/anthology/2020.emnlp-main.34 AUTHORS: Ryosuke Kohita, Akifumi Wachi, Yang Zhao, Ryuki Tachibana HIGHLIGHT: In this paper, we propose a new approach based on Q-learning with an edit-based summarization.
35, TITLE: Friendly Topic Assistant for Transformer Based Abstractive Summarization https://www.aclweb.org/anthology/2020.emnlp-main.35 AUTHORS: Zhengjue Wang, Zhibin Duan, Hao Zhang, Chaojie Wang, Long Tian, Bo Chen, Mingyuan Zhou HIGHLIGHT: To this end, we rearrange and explore the semantics learned by a topic model, and then propose a topic assistant (TA) including three modules.
36, TITLE: Contrastive Distillation on Intermediate Representations for Language Model Compression https://www.aclweb.org/anthology/2020.emnlp-main.36 AUTHORS: Siqi Sun, Zhe Gan, Yuwei Fang, Yu Cheng, Shuohang Wang, Jingjing Liu HIGHLIGHT: To achieve better distillation efficacy, we propose Contrastive Distillation on Intermediate Representations (CoDIR), a principled knowledge distillation framework where the student is trained to distill knowledge through intermediate layers of the teacher via a contrastive objective.
37, TITLE: TernaryBERT: Distillation-aware Ultra-low Bit BERT https://www.aclweb.org/anthology/2020.emnlp-main.37 AUTHORS: Wei Zhang, Lu Hou, Yichun Yin, Lifeng Shang, Xiao Chen, Xin Jiang, Qun Liu HIGHLIGHT: In this work, we propose TernaryBERT, which ternarizes the weights in a fine-tuned BERT model.
38, TITLE: Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks https://www.aclweb.org/anthology/2020.emnlp-main.38 AUTHORS: Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai, Andrew McCallum HIGHLIGHT: This paper proposes a self-supervised approach to generate a large, rich, meta-learning task distribution from unlabeled text.
39, TITLE: Efficient Meta Lifelong-Learning with Limited Memory https://www.aclweb.org/anthology/2020.emnlp-main.39 AUTHORS: Zirui Wang, Sanket Vaibhav Mehta, Barnabas Poczos, Jaime Carbonell HIGHLIGHT: In this paper, we identify three common principles of lifelong learning methods and propose an efficient meta- lifelong framework that combines them in a synergistic fashion.
40, TITLE: Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings https://www.aclweb.org/anthology/2020.emnlp-main.40 AUTHORS: Phillip Keung, Yichao Lu, Julian Salazar, Vikas Bhardwaj
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HIGHLIGHT: We show that the standard practice of using English dev accuracy for model selection in the zero-shot setting makes it difficult to obtain reproducible results on the MLDoc and XNLI tasks.
41, TITLE: A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT https://www.aclweb.org/anthology/2020.emnlp-main.41 AUTHORS: Masaaki Nagata, Katsuki Chousa, Masaaki Nishino HIGHLIGHT: We present a novel supervised word alignment method based on cross-language span prediction.
42, TITLE: Accurate Word Alignment Induction from Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.42 AUTHORS: Yun Chen, Yang Liu, Guanhua Chen, Xin Jiang, Qun Liu HIGHLIGHT: In this paper, we show that attention weights do capture accurate word alignments and propose two novel word alignment induction methods Shift-Att and Shift-AET.
43, TITLE: ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization https://www.aclweb.org/anthology/2020.emnlp-main.43 AUTHORS: Shiyue Zhang, Benjamin Frey, Mohit Bansal HIGHLIGHT: To help save this endangered language, we introduce ChrEn, a Cherokee-English parallel dataset, to facilitate machine translation research between Cherokee and English.
44, TITLE: Unsupervised Discovery of Implicit Gender Bias https://www.aclweb.org/anthology/2020.emnlp-main.44 AUTHORS: Anjalie Field, Yulia Tsvetkov HIGHLIGHT: We take an unsupervised approach to identifying gender bias against women at a comment level and present a model that can surface text likely to contain bias.
45, TITLE: Condolence and Empathy in Online Communities https://www.aclweb.org/anthology/2020.emnlp-main.45 AUTHORS: Naitian Zhou, David Jurgens HIGHLIGHT: Here, we develop computational tools to create a massive dataset of 11.4M expressions of distress and 2.8M corresponding offerings of condolence in order to examine the dynamics of condolence online.
46, TITLE: An Embedding Model for Estimating Legislative Preferences from the Frequency and Sentiment of Tweets https://www.aclweb.org/anthology/2020.emnlp-main.46 AUTHORS: Gregory Spell, Brian Guay, Sunshine Hillygus, Lawrence Carin HIGHLIGHT: In this paper we introduce a method of measuring more specific legislator attitudes using an alternative expression of preferences: tweeting.
47, TITLE: Measuring Information Propagation in Literary Social Networks https://www.aclweb.org/anthology/2020.emnlp-main.47 AUTHORS: Matthew Sims, David Bamman HIGHLIGHT: We describe a new pipeline for measuring information propagation in this domain and publish a new dataset for speaker attribution, enabling the evaluation of an important component of this pipeline on a wider range of literary texts than previously studied.
48, TITLE: Social Chemistry 101: Learning to Reason about Social and Moral Norms https://www.aclweb.org/anthology/2020.emnlp-main.48 AUTHORS: Maxwell Forbes, Jena D. Hwang, Vered Shwartz, Maarten Sap, Yejin Choi HIGHLIGHT: We present SOCIAL CHEMISTRY, a new conceptual formalism to study people's everyday social norms and moral judgments over a rich spectrum of real life situations described in natural language.
49, TITLE: Event Extraction by Answering (Almost) Natural Questions https://www.aclweb.org/anthology/2020.emnlp-main.49 AUTHORS: Xinya Du, Claire Cardie HIGHLIGHT: To avoid this issue, we introduce a new paradigm for event extraction by formulating it as a question answering (QA) task that extracts the event arguments in an end-to-end manner.
50, TITLE: Connecting the Dots: Event Graph Schema Induction with Path Language Modeling https://www.aclweb.org/anthology/2020.emnlp-main.50 AUTHORS: Manling Li, Qi Zeng, Ying Lin, Kyunghyun Cho, Heng Ji, Jonathan May, Nathanael Chambers, Clare Voss
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HIGHLIGHT: We propose a new Event Graph Schema, where two event types are connected through multiple paths involving entities that fill important roles in a coherent story.
51, TITLE: Joint Constrained Learning for Event-Event Relation Extraction https://www.aclweb.org/anthology/2020.emnlp-main.51 AUTHORS: Haoyu Wang, Muhao Chen, Hongming Zhang, Dan Roth HIGHLIGHT: Due to the lack of jointly labeled data for these relational phenomena and the restriction on the structures they articulate, we propose a joint constrained learning framework for modeling event-event relations.
52, TITLE: Incremental Event Detection via Knowledge Consolidation Networks https://www.aclweb.org/anthology/2020.emnlp-main.52 AUTHORS: Pengfei Cao, Yubo Chen, Jun Zhao, Taifeng Wang HIGHLIGHT: In this paper, we propose a Knowledge Consolidation Network (KCN) to address the above issues.
53, TITLE: Semi-supervised New Event Type Induction and Event Detection https://www.aclweb.org/anthology/2020.emnlp-main.53 AUTHORS: Lifu Huang, Heng Ji HIGHLIGHT: In this paper, we work on a new task of semi-supervised event type induction, aiming to automatically discover a set of unseen types from a given corpus by leveraging annotations available for a few seen types.
54, TITLE: Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph https://www.aclweb.org/anthology/2020.emnlp-main.54 AUTHORS: Haozhe Ji, Pei Ke, Shaohan Huang, Furu Wei, Xiaoyan Zhu, Minlie Huang HIGHLIGHT: In this paper, we propose Generation with Multi-Hop Reasoning Flow (GRF) that enables pre-trained models with dynamic multi-hop reasoning on multi-relational paths extracted from the external commonsense knowledge graph.
55, TITLE: Reformulating Unsupervised Style Transfer as Paraphrase Generation https://www.aclweb.org/anthology/2020.emnlp-main.55 AUTHORS: Kalpesh Krishna, John Wieting, Mohit Iyyer HIGHLIGHT: In this paper, we reformulate unsupervised style transfer as a paraphrase generation problem, and present a simple methodology based on fine-tuning pretrained language models on automatically generated paraphrase data.
56, TITLE: De-Biased Court's View Generation with Causality https://www.aclweb.org/anthology/2020.emnlp-main.56 AUTHORS: Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, Fei Wu HIGHLIGHT: In this paper, we propose a novel Attentional and Counterfactual based Natural Language Generation (AC- NLG) method, consisting of an attentional encoder and a pair of innovative counterfactual decoders.
57, TITLE: PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation https://www.aclweb.org/anthology/2020.emnlp-main.57 AUTHORS: Xinyu Hua, Lu Wang HIGHLIGHT: In this work, we present a novel content-controlled text generation framework, PAIR, with planning and iterative refinement, which is built upon a large model, BART.
58, TITLE: Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning https://www.aclweb.org/anthology/2020.emnlp-main.58 AUTHORS: Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena D. Hwang, Ronan Le Bras, Antoine Bosselut, Yejin Choi HIGHLIGHT: In this paper, we propose DeLorean, a new unsupervised decoding algorithm that can flexibly incorporate both the past and future contexts using only off-the-shelf, left-to-right language models and no supervision.
59, TITLE: Where Are You? Localization from Embodied Dialog https://www.aclweb.org/anthology/2020.emnlp-main.59 AUTHORS: Meera Hahn, Jacob Krantz, Dhruv Batra, Devi Parikh, James Rehg, Stefan Lee, Peter Anderson HIGHLIGHT: In this paper, we focus on the LED task - providing a strong baseline model with detailed ablations characterizing both dataset biases and the importance of various modeling choices.
60, TITLE: Learning to Represent Image and Text with Denotation Graph
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https://www.aclweb.org/anthology/2020.emnlp-main.60 AUTHORS: Bowen Zhang, Hexiang Hu, Vihan Jain, Eugene Ie, Fei Sha HIGHLIGHT: In this paper, we propose learning representations from a set of implied, visually grounded expressions between image and text, automatically mined from those datasets.
61, TITLE: Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning https://www.aclweb.org/anthology/2020.emnlp-main.61 AUTHORS: Zhiyuan Fang, Tejas Gokhale, Pratyay Banerjee, Chitta Baral, Yezhou Yang HIGHLIGHT: We present the first work on generating \textit{commonsense} captions directly from videos, to describe latent aspects such as intentions, effects, and attributes.
62, TITLE: Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think! https://www.aclweb.org/anthology/2020.emnlp-main.62 AUTHORS: Jack Hessel, Lillian Lee HIGHLIGHT: We propose a new diagnostic tool, empirical multimodally-additive function projection (EMAP), for isolating whether or not cross-modal interactions improve performance for a given model on a given task.
63, TITLE: MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.63 AUTHORS: Tejas Gokhale, Pratyay Banerjee, Chitta Baral, Yezhou Yang HIGHLIGHT: In this paper, we present \textit{MUTANT}, a training paradigm that exposes the model to perceptually similar, yet semantically distinct \textit{mutations} of the input, to improve OOD generalization, such as the VQA-CP challenge.
64, TITLE: Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning https://www.aclweb.org/anthology/2020.emnlp-main.64 AUTHORS: Haochen Liu, Wentao Wang, Yiqi Wang, Hui Liu, Zitao Liu, Jiliang Tang HIGHLIGHT: In this paper, we propose a novel adversarial learning framework Debiased-Chat to train dialogue models free from gender bias while keeping their performance.
65, TITLE: Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness https://www.aclweb.org/anthology/2020.emnlp-main.65 AUTHORS: Hyunwoo Kim, Byeongchang Kim, Gunhee Kim HIGHLIGHT: We explore the task of improving persona consistency of dialogue agents.
66, TITLE: TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue https://www.aclweb.org/anthology/2020.emnlp-main.66 AUTHORS: Chien-Sheng Wu, Steven C.H. Hoi, Richard Socher, Caiming Xiong HIGHLIGHT: In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling.
67, TITLE: RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task- Oriented Dialogue Modeling https://www.aclweb.org/anthology/2020.emnlp-main.67 AUTHORS: Jun Quan, Shian Zhang, Qian Cao, Zizhong Li, Deyi Xiong HIGHLIGHT: In order to alleviate the shortage of multi-domain data and to capture discourse phenomena for task-oriented dialogue modeling, we propose RiSAWOZ, a large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semantic Annotations.
68, TITLE: Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness https://www.aclweb.org/anthology/2020.emnlp-main.68 AUTHORS: Reina Akama, Sho Yokoi, Jun Suzuki, Kentaro Inui HIGHLIGHT: In this paper, we propose a method for scoring the quality of utterance pairs in terms of their connectivity and relatedness.
69, TITLE: Latent Geographical Factors for Analyzing the Evolution of Dialects in Contact https://www.aclweb.org/anthology/2020.emnlp-main.69 AUTHORS: Yugo Murawaki HIGHLIGHT: In this paper, we propose a probabilistic generative model that represents latent factors as geographical distributions.
70, TITLE: Predicting Reference: What do Language Models Learn about Discourse Models?
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https://www.aclweb.org/anthology/2020.emnlp-main.70 AUTHORS: Shiva Upadhye, Leon Bergen, Andrew Kehler HIGHLIGHT: We address this question by drawing on a rich psycholinguistic literature that has established how different contexts affect referential biases concerning who is likely to be referred to next.
71, TITLE: Word class flexibility: A deep contextualized approach https://www.aclweb.org/anthology/2020.emnlp-main.71 AUTHORS: Bai Li, Guillaume Thomas, Yang Xu, Frank Rudzicz HIGHLIGHT: We propose a principled methodology to explore regularity in word class flexibility.
72, TITLE: Shallow-to-Deep Training for Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.72 AUTHORS: Bei Li, Ziyang Wang, Hui Liu, Yufan Jiang, Quan Du, Tong Xiao, Huizhen Wang, Jingbo Zhu HIGHLIGHT: In this paper, we investigate the behavior of a well-tuned deep Transformer system.
73, TITLE: Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.73 AUTHORS: Jason Lee, Raphael Shu, Kyunghyun Cho HIGHLIGHT: We propose an efficient inference procedure for non-autoregressive machine translation that iteratively refines translation purely in the continuous space.
74, TITLE: Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers https://www.aclweb.org/anthology/2020.emnlp-main.74 AUTHORS: Yimeng Wu, Peyman Passban, Mehdi Rezagholizadeh, Qun Liu HIGHLIGHT: In this paper, we target low-resource settings and evaluate our translation engines for Portuguese?English, Turkish?English, and English?German directions.
75, TITLE: Multi-task Learning for Multilingual Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.75 AUTHORS: Yiren Wang, ChengXiang Zhai, Hany Hassan HIGHLIGHT: In this work, we propose a multi-task learning (MTL) framework that jointly trains the model with the translation task on bitext data and two denoising tasks on the monolingual data.
76, TITLE: Token-level Adaptive Training for Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.76 AUTHORS: Shuhao Gu, Jinchao Zhang, Fandong Meng, Yang Feng, Wanying Xie, Jie Zhou, Dong Yu HIGHLIGHT: In this paper, we explored target token-level adaptive objectives based on token frequencies to assign appropriate weights for each target token during training.
77, TITLE: Multi-Unit Transformers for Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.77 AUTHORS: Jianhao Yan, Fandong Meng, Jie Zhou HIGHLIGHT: In this paper, we propose the Multi-Unit Transformer (MUTE) , which aim to promote the expressiveness of the Transformer by introducing diverse and complementary units.
78, TITLE: On the Sparsity of Neural Machine Translation Models https://www.aclweb.org/anthology/2020.emnlp-main.78 AUTHORS: Yong Wang, Longyue Wang, Victor Li, Zhaopeng Tu HIGHLIGHT: In response to this problem, we empirically investigate whether the redundant parameters can be reused to achieve better performance.
79, TITLE: Incorporating a Local Translation Mechanism into Non-autoregressive Translation https://www.aclweb.org/anthology/2020.emnlp-main.79 AUTHORS: Xiang Kong, Zhisong Zhang, Eduard Hovy HIGHLIGHT: In this work, we introduce a novel local autoregressive translation (LAT) mechanism into non-autoregressive translation (NAT) models so as to capture local dependencies among target outputs.
80, TITLE: Self-Paced Learning for Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.80 AUTHORS: Yu Wan, Baosong Yang, Derek F. Wong, Yikai Zhou, Lidia S. Chao, Haibo Zhang, Boxing Chen
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HIGHLIGHT: We ameliorate this procedure with a more flexible manner by proposing self-paced learning, where NMT model is allowed to 1) automatically quantify the learning confidence over training examples; and 2) flexibly govern its learning via regulating the loss in each iteration step.
81, TITLE: Long-Short Term Masking Transformer: A Simple but Effective Baseline for Document-level Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.81 AUTHORS: Pei Zhang, Boxing Chen, Niyu Ge, Kai Fan HIGHLIGHT: In this paper, we research extensively the pros and cons of the standard transformer in document-level translation, and find that the auto-regressive property can simultaneously bring both the advantage of the consistency and the disadvantage of error accumulation.
82, TITLE: Generating Diverse Translation from Model Distribution with Dropout https://www.aclweb.org/anthology/2020.emnlp-main.82 AUTHORS: Xuanfu Wu, Yang Feng, Chenze Shao HIGHLIGHT: In this paper, we propose to generate diverse translations by deriving a large number of possible models with Bayesian modelling and sampling models from them for inference.
83, TITLE: Non-Autoregressive Machine Translation with Latent Alignments https://www.aclweb.org/anthology/2020.emnlp-main.83 AUTHORS: Chitwan Saharia, William Chan, Saurabh Saxena, Mohammad Norouzi HIGHLIGHT: This paper presents two strong methods, CTC and Imputer, for non-autoregressive machine translation that model latent alignments with dynamic programming.
84, TITLE: Look at the First Sentence: Position Bias in Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.84 AUTHORS: Miyoung Ko, Jinhyuk Lee, Hyunjae Kim, Gangwoo Kim, Jaewoo Kang HIGHLIGHT: In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions.
85, TITLE: ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning https://www.aclweb.org/anthology/2020.emnlp-main.85 AUTHORS: Michael Boratko, Xiang Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum HIGHLIGHT: This paper introduces a new question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations.
86, TITLE: IIRC: A Dataset of Incomplete Information Reading Comprehension Questions https://www.aclweb.org/anthology/2020.emnlp-main.86 AUTHORS: James Ferguson, Matt Gardner, Hannaneh Hajishirzi, Tushar Khot, Pradeep Dasigi HIGHLIGHT: To fill this gap, we present a dataset, IIRC, with more than 13K questions over paragraphs from English Wikipedia that provide only partial information to answer them, with the missing information occurring in one or more linked documents.
87, TITLE: Unsupervised Adaptation of Question Answering Systems via Generative Self-training https://www.aclweb.org/anthology/2020.emnlp-main.87 AUTHORS: Steven Rennie, Etienne Marcheret, Neil Mallinar, David Nahamoo, Vaibhava Goel HIGHLIGHT: In this paper we investigate the iterative generation of synthetic QA pairs as a way to realize unsupervised self adaptation.
88, TITLE: TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions https://www.aclweb.org/anthology/2020.emnlp-main.88 AUTHORS: Qiang Ning, Hao Wu, Rujun Han, Nanyun Peng, Matt Gardner, Dan Roth HIGHLIGHT: We introduce TORQUE, a new English reading comprehension benchmark built on 3.2k news snippets with 21k human-generated questions querying temporal relationships.
89, TITLE: ToTTo: A Controlled Table-To-Text Generation Dataset https://www.aclweb.org/anthology/2020.emnlp-main.89 AUTHORS: Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das
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HIGHLIGHT: We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
90, TITLE: ENT-DESC: Entity Description Generation by Exploring Knowledge Graph https://www.aclweb.org/anthology/2020.emnlp-main.90 AUTHORS: Liying Cheng, Dekun Wu, Lidong Bing, Yan Zhang, Zhanming Jie, Wei Lu, Luo Si HIGHLIGHT: In this paper, we introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text.
91, TITLE: Small but Mighty: New Benchmarks for Split and Rephrase https://www.aclweb.org/anthology/2020.emnlp-main.91 AUTHORS: Li Zhang, Huaiyu Zhu, Siddhartha Brahma, Yunyao Li HIGHLIGHT: We find that the widely used benchmark dataset universally contains easily exploitable syntactic cues caused by its automatic generation process.
92, TITLE: Online Back-Parsing for AMR-to-Text Generation https://www.aclweb.org/anthology/2020.emnlp-main.92 AUTHORS: Xuefeng Bai, Linfeng Song, Yue Zhang HIGHLIGHT: We propose a decoder that back predicts projected AMR graphs on the target sentence during text generation.
93, TITLE: Reading Between the Lines: Exploring Infilling in Visual Narratives https://www.aclweb.org/anthology/2020.emnlp-main.93 AUTHORS: Khyathi Raghavi Chandu, Ruo-Ping Dong, Alan W Black HIGHLIGHT: In this paper, we tackle this problem by using infilling techniques involving prediction of missing steps in a narrative while generating textual descriptions from a sequence of images.
94, TITLE: Acrostic Poem Generation https://www.aclweb.org/anthology/2020.emnlp-main.94 AUTHORS: Rajat Agarwal, Katharina Kann HIGHLIGHT: We propose a new task in the area of computational creativity: acrostic poem generation in English.
95, TITLE: Local Additivity Based Data Augmentation for Semi-supervised NER https://www.aclweb.org/anthology/2020.emnlp-main.95 AUTHORS: Jiaao Chen, Zhenghui Wang, Ran Tian, Zichao Yang, Diyi Yang HIGHLIGHT: In this work, to alleviate the dependence on labeled data, we propose a Local Additivity based Data Augmentation (LADA) method for semi-supervised NER, in which we create virtual samples by interpolating sequences close to each other.
96, TITLE: Grounded Compositional Outputs for Adaptive Language Modeling https://www.aclweb.org/anthology/2020.emnlp-main.96 AUTHORS: Nikolaos Pappas, Phoebe Mulcaire, Noah A. Smith HIGHLIGHT: In this work, we go one step beyond and propose a fully compositional output embedding layer for language models, which is further grounded in information from a structured lexicon (WordNet), namely semantically related words and free- text definitions.
97, TITLE: SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness https://www.aclweb.org/anthology/2020.emnlp-main.97 AUTHORS: Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi HIGHLIGHT: We introduce SSMBA, a data augmentation method for generating synthetic training examples by using a pair of corruption and reconstruction functions to move randomly on a data manifold.
98, TITLE: SetConv: A New Approach for Learning from Imbalanced Data https://www.aclweb.org/anthology/2020.emnlp-main.98 AUTHORS: Yang Gao, Yi-Fan Li, Yu Lin, Charu Aggarwal, Latifur Khan HIGHLIGHT: To address this problem, we propose a set convolution (SetConv) operation and an episodic training strategy to extract a single representative for each class, so that classifiers can later be trained on a balanced class distribution.
99, TITLE: Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.99 AUTHORS: Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, Peifeng Wang, Jun Yan, Xiang Ren
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HIGHLIGHT: In this paper, we propose a novel knowledge-aware approach that equips pre-trained language models (PTLMs) has with a multi-hop relational reasoning module, named multi-hop graph relation network (MHGRN).
100, TITLE: Improving Bilingual Lexicon Induction for Low Frequency Words https://www.aclweb.org/anthology/2020.emnlp-main.100 AUTHORS: Jiaji Huang, Xingyu Cai, Kenneth Church HIGHLIGHT: This paper designs a Monolingual Lexicon Induction task and observes that two factors accompany the degraded accuracy of bilingual lexicon induction for rare words.
101, TITLE: Learning VAE-LDA Models with Rounded Reparameterization Trick https://www.aclweb.org/anthology/2020.emnlp-main.101 AUTHORS: Runzhi Tian, Yongyi Mao, Richong Zhang HIGHLIGHT: In this work, we propose a new method, which we call Rounded Reparameterization Trick (RRT), to reparameterize Dirichlet distributions for the learning of VAE-LDA models.
102, TITLE: Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data https://www.aclweb.org/anthology/2020.emnlp-main.102 AUTHORS: Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, Chao Zhang HIGHLIGHT: To mitigate this issue, we propose a regularized fine-tuning method.
103, TITLE: Scaling Hidden Markov Language Models https://www.aclweb.org/anthology/2020.emnlp-main.103 AUTHORS: Justin Chiu, Alexander Rush HIGHLIGHT: We propose methods for scaling HMMs to massive state spaces while maintaining efficient exact inference, a compact parameterization, and effective regularization.
104, TITLE: Coding Textual Inputs Boosts the Accuracy of Neural Networks https://www.aclweb.org/anthology/2020.emnlp-main.104 AUTHORS: Abdul Rafae Khan, Jia Xu, Weiwei Sun HIGHLIGHT: As "alternatives" to a text representation, we introduce Soundex, MetaPhone, NYSIIS, logogram to NLP, and develop fixed-output-length coding and its extension using Huffman coding.
105, TITLE: Learning from Task Descriptions https://www.aclweb.org/anthology/2020.emnlp-main.105 AUTHORS: Orion Weller, Nicholas Lourie, Matt Gardner, Matthew Peters HIGHLIGHT: To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area.
106, TITLE: Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics https://www.aclweb.org/anthology/2020.emnlp-main.106 AUTHORS: Keyang Ding, Jing Li, Yuji Zhang HIGHLIGHT: This paper studies social emotions to online discussion topics.
107, TITLE: Named Entity Recognition for Social Media Texts with Semantic Augmentation https://www.aclweb.org/anthology/2020.emnlp-main.107 AUTHORS: Yuyang Nie, Yuanhe Tian, Xiang Wan, Yan Song, Bo Dai HIGHLIGHT: In this paper, we propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account.
108, TITLE: Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations https://www.aclweb.org/anthology/2020.emnlp-main.108 AUTHORS: Jianfei Yu, Jing Jiang, Ling Min Serena Khoo, Hai Leong Chieu, Rui Xia HIGHLIGHT: Therefore, in this paper, to extend BERT to obtain thread representations, we first propose a Hierarchical Transformer, which divides each long thread into shorter subthreads, and employs BERT to separately represent each subthread, followed by a global Transformer layer to encode all the subthreads.
109, TITLE: Social Media Attributions in the Context of Water Crisis https://www.aclweb.org/anthology/2020.emnlp-main.109 AUTHORS: Rupak Sarkar, Sayantan Mahinder, Hirak Sarkar, Ashiqur KhudaBukhsh
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HIGHLIGHT: In this paper, we explore the viability of using unstructured, noisy social media data to complement traditional surveys through automatically extracting attribution factors.
110, TITLE: On the Reliability and Validity of Detecting Approval of Political Actors in Tweets https://www.aclweb.org/anthology/2020.emnlp-main.110 AUTHORS: Indira Sen, Fabian Flöck, Claudia Wagner HIGHLIGHT: In this work, we attempt to gauge the efficacy of untargeted sentiment, targeted sentiment, and stance detection methods in labeling various political actors' approval by benchmarking them across several datasets.
111, TITLE: Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text https://www.aclweb.org/anthology/2020.emnlp-main.111 AUTHORS: Dongfang Li, Baotian Hu, Qingcai Chen, Weihua Peng, Anqi Wang HIGHLIGHT: As an effort, we first collect a large scale medical multi-choice question dataset (more than 21k instances) for the National Licensed Pharmacist Examination in China.
112, TITLE: Generating Radiology Reports via Memory-driven Transformer https://www.aclweb.org/anthology/2020.emnlp-main.112 AUTHORS: Zhihong Chen, Yan Song, Tsung-Hui Chang, Xiang Wan HIGHLIGHT: In this paper, we propose to generate radiology reports with memory-driven Transformer, where a relational memory is designed to record key information of the generation process and a memory-driven conditional layer normalization is applied to incorporating the memory into the decoder of Transformer.
113, TITLE: Planning and Generating Natural and Diverse Disfluent Texts as Augmentation for Disfluency Detection https://www.aclweb.org/anthology/2020.emnlp-main.113 AUTHORS: Jingfeng Yang, Diyi Yang, Zhaoran Ma HIGHLIGHT: In this work, we propose a simple Planner-Generator based disfluency generation model to generate natural and diverse disfluent texts as augmented data, where the Planner decides on where to insert disfluent segments and the Generator follows the prediction to generate corresponding disfluent segments.
114, TITLE: Predicting Clinical Trial Results by Implicit Evidence Integration https://www.aclweb.org/anthology/2020.emnlp-main.114 AUTHORS: Qiao Jin, Chuanqi Tan, Mosha Chen, Xiaozhong Liu, Songfang Huang HIGHLIGHT: To optimize the design of clinical trials, we introduce a novel Clinical Trial Result Prediction (CTRP) task.
115, TITLE: Explainable Clinical Decision Support from Text https://www.aclweb.org/anthology/2020.emnlp-main.115 AUTHORS: Jinyue Feng, Chantal Shaib, Frank Rudzicz HIGHLIGHT: We propose a hierarchical CNN-transformer model with explicit attention as an interpretable, multi-task clinical language model, which achieves an AUROC of 0.75 and 0.78 on sepsis and mortality prediction, respectively.
116, TITLE: A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization https://www.aclweb.org/anthology/2020.emnlp-main.116 AUTHORS: Jinghui Yan, Yining Wang, Lu Xiang, Yu Zhou, Chengqing Zong HIGHLIGHT: In this paper, we focus on Chinese medical procedure entity normalization.
117, TITLE: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT https://www.aclweb.org/anthology/2020.emnlp-main.117 AUTHORS: Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Ng, Matthew Lungren HIGHLIGHT: In this work, we introduce a BERT-based approach to medical image report labeling that exploits both the scale of available rule-based systems and the quality of expert annotations.
118, TITLE: Benchmarking Meaning Representations in Neural Semantic Parsing https://www.aclweb.org/anthology/2020.emnlp-main.118 AUTHORS: Jiaqi Guo, Qian Liu, Jian-Guang Lou, Zhenwen Li, Xueqing Liu, Tao Xie, Ting Liu HIGHLIGHT: Upon identifying these gaps, we propose , a new unified benchmark on meaning representations, by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines.
119, TITLE: Analogous Process Structure Induction for Sub-event Sequence Prediction https://www.aclweb.org/anthology/2020.emnlp-main.119 AUTHORS: Hongming Zhang, Muhao Chen, Haoyu Wang, Yangqiu Song, Dan Roth
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HIGHLIGHT: In this paper, we propose an Analogous Process Structure Induction (APSI) framework, which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub-event sequence of previously unseen open-domain processes.
120, TITLE: SLM: Learning a Discourse Language Representation with Sentence Unshuffling https://www.aclweb.org/anthology/2020.emnlp-main.120 AUTHORS: Haejun Lee, Drew A. Hudson, Kangwook Lee, Christopher D. Manning HIGHLIGHT: We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner.
121, TITLE: Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank https://www.aclweb.org/anthology/2020.emnlp-main.121 AUTHORS: Eleftheria Briakou, Marine Carpuat HIGHLIGHT: We introduce a training strategy for multilingual BERT models by learning to rank synthetic divergent examples of varying granularity.
122, TITLE: A Bilingual Generative Transformer for Semantic Sentence Embedding https://www.aclweb.org/anthology/2020.emnlp-main.122 AUTHORS: John Wieting, Graham Neubig, Taylor Berg-Kirkpatrick HIGHLIGHT: We propose a deep latent variable model that attempts to perform source separation on parallel sentences, isolating what they have in common in a latent semantic vector, and explaining what is left over with language-specific latent vectors.
123, TITLE: Semantically Inspired AMR Alignment for the Portuguese Language https://www.aclweb.org/anthology/2020.emnlp-main.123 AUTHORS: Rafael Anchiêta, Thiago Pardo HIGHLIGHT: Aiming to fulfill this gap, we developed an alignment method for the Portuguese language based on a more semantically matched word-concept pair.
124, TITLE: An Unsupervised Sentence Embedding Method by Mutual Information Maximization https://www.aclweb.org/anthology/2020.emnlp-main.124 AUTHORS: Yan Zhang, Ruidan He, Zuozhu Liu, Kwan Hui Lim, Lidong Bing HIGHLIGHT: In this paper, we propose a lightweight extension on top of BERT and a novel self-supervised learning objective based on mutual information maximization strategies to derive meaningful sentence embeddings in an unsupervised manner.
125, TITLE: Compositional Phrase Alignment and Beyond https://www.aclweb.org/anthology/2020.emnlp-main.125 AUTHORS: Yuki Arase, Jun’ichi Tsujii HIGHLIGHT: We address the phrase alignment problem by combining an unordered tree mapping algorithm and phrase representation modelling that explicitly embeds the similarity distribution in the sentences onto powerful contextualized representations.
126, TITLE: Table Fact Verification with Structure-Aware Transformer https://www.aclweb.org/anthology/2020.emnlp-main.126 AUTHORS: Hongzhi Zhang, Yingyao Wang, Sirui Wang, Xuezhi Cao, Fuzheng Zhang, Zhongyuan Wang HIGHLIGHT: To better utilize pre-trained transformers for table representation, we propose a Structure-Aware Transformer (SAT), which injects the table structural information into the mask of the self-attention layer.
127, TITLE: Double Graph Based Reasoning for Document-level Relation Extraction https://www.aclweb.org/anthology/2020.emnlp-main.127 AUTHORS: Shuang Zeng, Runxin Xu, Baobao Chang, Lei Li HIGHLIGHT: In this paper, we propose Graph Aggregation-and-Inference Network (GAIN), a method to recognize such relations for long paragraphs.
128, TITLE: Event Extraction as Machine Reading Comprehension https://www.aclweb.org/anthology/2020.emnlp-main.128 AUTHORS: Jian Liu, Yubo Chen, Kang Liu, Wei Bi, Xiaojiang Liu HIGHLIGHT: In this paper, we propose a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension problem (MRC).
129, TITLE: MAVEN: A Massive General Domain Event Detection Dataset
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https://www.aclweb.org/anthology/2020.emnlp-main.129 AUTHORS: Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou HIGHLIGHT: To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4,480 Wikipedia documents, 118,732 event mention instances, and 168 event types.
130, TITLE: Knowledge Graph Alignment with Entity-Pair Embedding https://www.aclweb.org/anthology/2020.emnlp-main.130 AUTHORS: Zhichun Wang, Jinjian Yang, Xiaoju Ye HIGHLIGHT: In this work, we present a new approach that directly learns embeddings of entity-pairs for KG alignment.
131, TITLE: Adaptive Attentional Network for Few-Shot Knowledge Graph Completion https://www.aclweb.org/anthology/2020.emnlp-main.131 AUTHORS: Jiawei Sheng, Shu Guo, Zhenyu Chen, Juwei Yue, Lihong Wang, Tingwen Liu, Hongbo Xu HIGHLIGHT: This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations.
132, TITLE: Pre-training Entity Relation Encoder with Intra-span and Inter-span Information https://www.aclweb.org/anthology/2020.emnlp-main.132 AUTHORS: Yijun Wang, Changzhi Sun, Yuanbin Wu, Junchi Yan, Peng Gao, Guotong Xie HIGHLIGHT: In this paper, we integrate span-related information into pre-trained encoder for entity relation extraction task.
133, TITLE: Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders https://www.aclweb.org/anthology/2020.emnlp-main.133 AUTHORS: Jue Wang, Wei Lu HIGHLIGHT: In this work, we propose the novel table-sequence encoders where two different encoders - a table encoder and a sequence encoder are designed to help each other in the representation learning process.
134, TITLE: Beyond [CLS] through Ranking by Generation https://www.aclweb.org/anthology/2020.emnlp-main.134 AUTHORS: Cicero Nogueira dos Santos, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, Bing Xiang HIGHLIGHT: In this work, we revisit the generative framework for information retrieval and show that our generative approaches are as effective as state-of-the-art semantic similarity-based discriminative models for the answer selection task.
135, TITLE: Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too! https://www.aclweb.org/anthology/2020.emnlp-main.135 AUTHORS: Suzanna Sia, Ayush Dalmia, Sabrina J. Mielke HIGHLIGHT: The dominant approach is to use probabilistic topic models that posit a generative story, but in this paper we propose an alternative way to obtain topics: clustering pre-trained word embeddings while incorporating document information for weighted clustering and reranking top words.
136, TITLE: Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning https://www.aclweb.org/anthology/2020.emnlp-main.136 AUTHORS: Yuning Mao, Yanru Qu, Yiqing Xie, Xiang Ren, Jiawei Han HIGHLIGHT: To close the gap, we present RL-MMR, Maximal Margin Relevance-guided Reinforcement Learning for MDS, which unifies advanced neural SDS methods and statistical measures used in classical MDS.
137, TITLE: Improving Neural Topic Models using Knowledge Distillation https://www.aclweb.org/anthology/2020.emnlp-main.137 AUTHORS: Alexander Miserlis Hoyle, Pranav Goel, Philip Resnik HIGHLIGHT: We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers.
138, TITLE: Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder https://www.aclweb.org/anthology/2020.emnlp-main.138 AUTHORS: Xiaobao Wu, Chunping Li, Yan Zhu, Yishu Miao HIGHLIGHT: In this paper, to address this issue, we propose a novel neural topic model in the framework of autoencoding with a new topic distribution quantization approach generating peakier distributions that are more appropriate for modeling short texts.
139, TITLE: Querying Across Genres for Medical Claims in News
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https://www.aclweb.org/anthology/2020.emnlp-main.139 AUTHORS: Chaoyuan Zuo, Narayan Acharya, Ritwik Banerjee HIGHLIGHT: We present a query-based biomedical information retrieval task across two vastly different genres - newswire and research literature - where the goal is to find the research publication that supports the primary claim made in a health-related news article.
140, TITLE: Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction https://www.aclweb.org/anthology/2020.emnlp-main.140 AUTHORS: Yansen Wang, Zhen Fan, Carolyn Rose HIGHLIGHT: In this work, we propose a modeling approach that leverages these multi-modal signals to aid in the KPE task.
141, TITLE: CMU-MOSEAS: A Multimodal Language Dataset for Spanish, Portuguese, German and French https://www.aclweb.org/anthology/2020.emnlp-main.141 AUTHORS: AmirAli Bagher Zadeh, Yansheng Cao, Simon Hessner, Paul Pu Liang, Soujanya Poria, Louis-Philippe Morency HIGHLIGHT: As a step towards building more equitable and inclusive multimodal systems, we introduce the first large-scale multimodal language dataset for Spanish, Portuguese, German and French.
142, TITLE: Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection https://www.aclweb.org/anthology/2020.emnlp-main.142 AUTHORS: Shaolei Wang, Zhongyuan Wang, Wanxiang Che, Ting Liu HIGHLIGHT: In this work, we explore the unsupervised learning paradigm which can potentially work with unlabeled text corpora that are cheaper and easier to obtain.
143, TITLE: Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis https://www.aclweb.org/anthology/2020.emnlp-main.143 AUTHORS: Yao-Hung Hubert Tsai, Martin Ma, Muqiao Yang, Ruslan Salakhutdinov, Louis-Philippe Morency HIGHLIGHT: In this paper we propose, which dynamically adjusts weights between input modalities and output representations differently for each input sample.
144, TITLE: Multistage Fusion with Forget Gate for Multimodal Summarization in Open-Domain Videos https://www.aclweb.org/anthology/2020.emnlp-main.144 AUTHORS: Nayu Liu, Xian Sun, Hongfeng Yu, Wenkai Zhang, Guangluan Xu HIGHLIGHT: To address these two issues, we propose a multistage fusion network with the fusion forget gate module, which builds upon this approach by modeling fine-grained interactions between the modalities through a multistep fusion schema and controlling the flow of redundant information between multimodal long sequences via a forgetting module.
145, TITLE: BiST: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues https://www.aclweb.org/anthology/2020.emnlp-main.145 AUTHORS: Hung Le, Doyen Sahoo, Nancy Chen, Steven C.H. Hoi HIGHLIGHT: To address this drawback, we proposed Bi-directional Spatio-Temporal Learning (BiST), a vision-language neural framework for high-resolution queries in videos based on textual cues.
146, TITLE: UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues https://www.aclweb.org/anthology/2020.emnlp-main.146 AUTHORS: Hung Le, Doyen Sahoo, Chenghao Liu, Nancy Chen, Steven C.H. Hoi HIGHLIGHT: Unlike the existing approaches that are often designed to train each module separately, we propose "UniConv" - a novel unified neural architecture for end-to-end conversational systems in multi-domain task-oriented dialogues, which is designed to jointly train (i) a Bi-level State Tracker which tracks dialogue states by learning signals at both slot and domain level independently, and (ii) a Joint Dialogue Act and Response Generator which incorporates information from various input components and models dialogue acts and target responses simultaneously.
147, TITLE: GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems https://www.aclweb.org/anthology/2020.emnlp-main.147 AUTHORS: Shiquan Yang, Rui Zhang, Sarah Erfani HIGHLIGHT: In this paper, we address these two challenges by exploiting the graph structural information in the knowledge base and in the dependency parsing tree of the dialogue.
148, TITLE: Structured Attention for Unsupervised Dialogue Structure Induction https://www.aclweb.org/anthology/2020.emnlp-main.148 AUTHORS: Liang Qiu, Yizhou Zhao, Weiyan Shi, Yuan Liang, Feng Shi, Tao Yuan, Zhou Yu, Song-Chun Zhu
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HIGHLIGHT: In this work, we propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion.
149, TITLE: Cross Copy Network for Dialogue Generation https://www.aclweb.org/anthology/2020.emnlp-main.149 AUTHORS: Changzhen Ji, Xin Zhou, Yating Zhang, Xiaozhong Liu, Changlong Sun, Conghui Zhu, Tiejun Zhao HIGHLIGHT: In this paper, we propose a novel network architecture - Cross Copy Networks (CCN) to explore the current dialog context and similar dialogue instances' logical structure simultaneously.
150, TITLE: Multi-turn Response Selection using Dialogue Dependency Relations https://www.aclweb.org/anthology/2020.emnlp-main.150 AUTHORS: Qi Jia, Yizhu Liu, Siyu Ren, Kenny Zhu, Haifeng Tang HIGHLIGHT: In this paper, we propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations.
151, TITLE: Parallel Interactive Networks for Multi-Domain Dialogue State Generation https://www.aclweb.org/anthology/2020.emnlp-main.151 AUTHORS: Junfan Chen, Richong Zhang, Yongyi Mao, Jie Xu HIGHLIGHT: In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies.
152, TITLE: SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling https://www.aclweb.org/anthology/2020.emnlp-main.152 AUTHORS: Di Wu, Liang Ding, Fan Lu, Jian Xie HIGHLIGHT: In this paper, we propose a novel non-autoregressive model named SlotRefine for joint intent detection and slot filling.
153, TITLE: An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction https://www.aclweb.org/anthology/2020.emnlp-main.153 AUTHORS: Bhargavi Paranjape, Mandar Joshi, John Thickstun, Hannaneh Hajishirzi, Luke Zettlemoyer HIGHLIGHT: In this paper, we show that it is possible to better manage the trade-off between concise explanations and high task accuracy by optimizing a bound on the Information Bottleneck (IB) objective.
154, TITLE: CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models https://www.aclweb.org/anthology/2020.emnlp-main.154 AUTHORS: Nikita Nangia, Clara Vania, Rasika Bhalerao, Samuel R. Bowman HIGHLIGHT: To measure some forms of social bias in language models against protected demographic groups in the US, we introduce the Crowdsourced Stereotype Pairs benchmark (CrowS-Pairs).
155, TITLE: LOGAN: Local Group Bias Detection by Clustering https://www.aclweb.org/anthology/2020.emnlp-main.155 AUTHORS: Jieyu Zhao, Kai-Wei Chang HIGHLIGHT: To analyze and detect such local bias, we propose LOGAN, a new bias detection technique based on clustering.
156, TITLE: RNNs can generate bounded hierarchical languages with optimal memory https://www.aclweb.org/anthology/2020.emnlp-main.156 AUTHORS: John Hewitt, Michael Hahn, Surya Ganguli, Percy Liang, Christopher D. Manning HIGHLIGHT: We introduce Dyck-$(k,m)$, the language of well-nested brackets (of $k$ types) and $m$-bounded nesting depth, reflecting the bounded memory needs and long-distance dependencies of natural language syntax.
157, TITLE: Detecting Independent Pronoun Bias with Partially-Synthetic Data Generation https://www.aclweb.org/anthology/2020.emnlp-main.157 AUTHORS: Robert Munro, Alex (Carmen) Morrison HIGHLIGHT: We introduce a new technique for measuring bias in models, using Bayesian approximations to generate partially-synthetic data from the model itself.
158, TITLE: Visually Grounded Continual Learning of Compositional Phrases https://www.aclweb.org/anthology/2020.emnlp-main.158 AUTHORS: Xisen Jin, Junyi Du, Arka Sadhu, Ram Nevatia, Xiang Ren
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HIGHLIGHT: To study this human-like language acquisition ability, we present VisCOLL, a visually grounded language learning task, which simulates the continual acquisition of compositional phrases from streaming visual scenes.
159, TITLE: MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding https://www.aclweb.org/anthology/2020.emnlp-main.159 AUTHORS: Qinxin Wang, Hao Tan, Sheng Shen, Michael Mahoney, Zhewei Yao HIGHLIGHT: Given difficulties in annotating phrase-to-object datasets at scale, we develop a Multimodal Alignment Framework (MAF) to leverage more widely-available caption-image datasets, which can then be used as a form of weak supervision.
160, TITLE: Domain-Specific Lexical Grounding in Noisy Visual-Textual Documents https://www.aclweb.org/anthology/2020.emnlp-main.160 AUTHORS: Gregory Yauney, Jack Hessel, David Mimno HIGHLIGHT: We present a simple unsupervised clustering-based method that increases precision and recall beyond object detection and image tagging baselines when evaluated on labeled subsets of the dataset.
161, TITLE: HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training https://www.aclweb.org/anthology/2020.emnlp-main.161 AUTHORS: Linjie Li, Yen-Chun Chen, Yu Cheng, Zhe Gan, Licheng Yu, Jingjing Liu HIGHLIGHT: We present HERO, a novel framework for large-scale video+language omni-representation learning.
162, TITLE: Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision https://www.aclweb.org/anthology/2020.emnlp-main.162 AUTHORS: Hao Tan, Mohit Bansal HIGHLIGHT: Therefore, we develop a technique named "vokenization" that extrapolates multimodal alignments to language- only data by contextually mapping language tokens to their related images (which we call "vokens").
163, TITLE: Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News https://www.aclweb.org/anthology/2020.emnlp-main.163 AUTHORS: Reuben Tan, Bryan Plummer, Kate Saenko HIGHLIGHT: In this paper, we introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions.
164, TITLE: Enhancing Aspect Term Extraction with Soft Prototypes https://www.aclweb.org/anthology/2020.emnlp-main.164 AUTHORS: Zhuang Chen, Tieyun Qian HIGHLIGHT: In this paper, we propose to tackle this problem by correlating words with each other through soft prototypes.
165, TITLE: FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction https://www.aclweb.org/anthology/2020.emnlp-main.165 AUTHORS: Dianbo Sui, Yubo Chen, Jun Zhao, Yantao Jia, Yuantao Xie, Weijian Sun HIGHLIGHT: In this paper, we propose a privacy-preserving medical relation extraction model based on federated learning, which enables training a central model with no single piece of private local data being shared or exchanged.
166, TITLE: Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product https://www.aclweb.org/anthology/2020.emnlp-main.166 AUTHORS: Tiangang Zhu, Yue Wang, Haoran Li, Youzheng Wu, Xiaodong He, Bowen Zhou HIGHLIGHT: In this paper, we propose a multimodal method to jointly predict product attributes and extract values from textual product descriptions with the help of the product images.
167, TITLE: A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression https://www.aclweb.org/anthology/2020.emnlp-main.167 AUTHORS: Mingming Sun, Wenyue Hua, Zoey Liu, Xin Wang, Kangjie Zheng, Ping Li HIGHLIGHT: This paper proposes a new pipeline to build OIE systems, where an Open-domain Information eXpression (OIX) task is proposed to provide a platform for all OIE strategies.
168, TITLE: Retrofitting Structure-aware Transformer Language Model for End Tasks https://www.aclweb.org/anthology/2020.emnlp-main.168 AUTHORS: Hao Fei, Yafeng Ren, Donghong Ji HIGHLIGHT: We consider retrofitting structure-aware Transformer language model for facilitating end tasks by proposing to exploit syntactic distance to encode both the phrasal constituency and dependency connection into the language model.
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169, TITLE: Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation https://www.aclweb.org/anthology/2020.emnlp-main.169 AUTHORS: Yan Zhang, Zhijiang Guo, Zhiyang Teng, Wei Lu, Shay B. Cohen, Zuozhu Liu, Lidong Bing HIGHLIGHT: In this paper, we introduce a dynamic fusion mechanism, proposing Lightweight Dynamic Graph Convolutional Networks (LDGCNs) that capture richer non-local interactions by synthesizing higher order information from the input graphs.
170, TITLE: If beam search is the answer, what was the question? https://www.aclweb.org/anthology/2020.emnlp-main.170 AUTHORS: Clara Meister, Ryan Cotterell, Tim Vieira HIGHLIGHT: We frame beam search as the exact solution to a different decoding objective in order to gain insights into why high probability under a model alone may not indicate adequacy.
171, TITLE: Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning https://www.aclweb.org/anthology/2020.emnlp-main.171 AUTHORS: Tsvetomila Mihaylova, Vlad Niculae, André F. T. Martins HIGHLIGHT: In this paper, we focus on surrogate gradients, a popular strategy to deal with this problem.
172, TITLE: Is the Best Better? Bayesian Statistical Model Comparison for Natural Language Processing https://www.aclweb.org/anthology/2020.emnlp-main.172 AUTHORS: Piotr Szyma?ski, Kyle Gorman HIGHLIGHT: We propose a Bayesian statistical model comparison technique which uses k-fold cross-validation across multiple data sets to estimate the likelihood that one model will outperform the other, or that the two will produce practically equivalent results.
173, TITLE: Exploring Logically Dependent Multi-task Learning with Causal Inference https://www.aclweb.org/anthology/2020.emnlp-main.173 AUTHORS: Wenqing Chen, Jidong Tian, Liqiang Xiao, Hao He, Yaohui Jin HIGHLIGHT: In this paper, we view logically dependent MTL from the perspective of causal inference and suggest a mediation assumption instead of the confounding assumption in conventional MTL models.
174, TITLE: Masking as an Efficient Alternative to Finetuning for Pretrained Language Models https://www.aclweb.org/anthology/2020.emnlp-main.174 AUTHORS: Mengjie Zhao, Tao Lin, Fei Mi, Martin Jaggi, Hinrich Schütze HIGHLIGHT: We present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning.
175, TITLE: Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning https://www.aclweb.org/anthology/2020.emnlp-main.175 AUTHORS: Xiaomian Kang, Yang Zhao, Jiajun Zhang, Chengqing Zong HIGHLIGHT: To address this problem, we propose an effective approach to select dynamic context so that the document-level translation model can utilize the more useful selected context sentences to produce better translations.
176, TITLE: Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.176 AUTHORS: Wenxiang Jiao, Xing Wang, Shilin He, Irwin King, Michael Lyu, Zhaopeng Tu HIGHLIGHT: In this work, we explore to identify the inactive training examples which contribute less to the model performance, and show that the existence of inactive examples depends on the data distribution.
177, TITLE: Pronoun-Targeted Fine-tuning for NMT with Hybrid Losses https://www.aclweb.org/anthology/2020.emnlp-main.177 AUTHORS: Prathyusha Jwalapuram, Shafiq Joty, Youlin Shen HIGHLIGHT: We introduce a class of conditional generative-discriminative hybrid losses that we use to fine-tune a trained machine translation model.
178, TITLE: Learning Adaptive Segmentation Policy for Simultaneous Translation https://www.aclweb.org/anthology/2020.emnlp-main.178 AUTHORS: Ruiqing Zhang, Chuanqiang Zhang, Zhongjun He, Hua Wu, Haifeng Wang HIGHLIGHT: Inspired by human interpreters, we propose a novel adaptive segmentation policy for simultaneous translation.
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179, TITLE: Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages https://www.aclweb.org/anthology/2020.emnlp-main.179 AUTHORS: Zheng Li, Mukul Kumar, William Headden, Bing Yin, Ying Wei, Yu Zhang, Qiang Yang HIGHLIGHT: To address the issues, we propose a meta graph learning (MGL) method.
180, TITLE: UDapter: Language Adaptation for Truly Universal Dependency Parsing https://www.aclweb.org/anthology/2020.emnlp-main.180 AUTHORS: Ahmet Üstün, Arianna Bisazza, Gosse Bouma, Gertjan van Noord HIGHLIGHT: To address this, we propose a novel multilingual task adaptation approach based on contextual parameter generation and adapter modules.
181, TITLE: Uncertainty-Aware Label Refinement for Sequence Labeling https://www.aclweb.org/anthology/2020.emnlp-main.181 AUTHORS: Tao Gui, Jiacheng Ye, Qi Zhang, Zhengyan Li, Zichu Fei, Yeyun Gong, Xuanjing Huang HIGHLIGHT: In this work, we introduce a novel two-stage label decoding framework to model long-term label dependencies, while being much more computationally efficient.
182, TITLE: Adversarial Attack and Defense of Structured Prediction Models https://www.aclweb.org/anthology/2020.emnlp-main.182 AUTHORS: Wenjuan Han, Liwen Zhang, Yong Jiang, Kewei Tu HIGHLIGHT: In this paper, we investigate attacks and defenses for structured prediction tasks in NLP.
183, TITLE: Position-Aware Tagging for Aspect Sentiment Triplet Extraction https://www.aclweb.org/anthology/2020.emnlp-main.183 AUTHORS: Lu Xu, Hao Li, Wei Lu, Lidong Bing HIGHLIGHT: In this work, we propose the first end-to-end model with a novel position-aware tagging scheme that is capable of jointly extracting the triplets.
184, TITLE: Simultaneous Machine Translation with Visual Context https://www.aclweb.org/anthology/2020.emnlp-main.184 AUTHORS: Ozan Caglayan, Julia Ive, Veneta Haralampieva, Pranava Madhyastha, Loïc Barrault, Lucia Specia HIGHLIGHT: In this paper, we seek to understand whether the addition of visual information can compensate for the missing source context.
185, TITLE: XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning https://www.aclweb.org/anthology/2020.emnlp-main.185 AUTHORS: Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, Qianchu Liu, Ivan Vuli?, Anna Korhonen HIGHLIGHT: Motivated by both demands, we introduce Cross-lingual Choice of Plausible Alternatives (XCOPA), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, which includes resource-poor languages like Eastern Apur{\'\i}mac Quechua and Haitian Creole.
186, TITLE: The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures https://www.aclweb.org/anthology/2020.emnlp-main.186 AUTHORS: Haim Dubossarsky, Ivan Vuli?, Roi Reichart, Anna Korhonen HIGHLIGHT: In this work we present a large-scale study focused on the correlations between monolingual embedding space similarity and task performance, covering thousands of language pairs and four different tasks: BLI, parsing, POS tagging and MT. We hypothesize that statistics of the spectrum of each monolingual embedding space indicate how well they can be aligned.
187, TITLE: Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations https://www.aclweb.org/anthology/2020.emnlp-main.187 AUTHORS: Arturo Oncevay, Barry Haddow, Alexandra Birch HIGHLIGHT: We propose to fuse both views using singular vector canonical correlation analysis and study what kind of information is induced from each source.
188, TITLE: AnswerFact: Fact Checking in Product Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.188 AUTHORS: Wenxuan Zhang, Yang Deng, Jing Ma, Wai Lam HIGHLIGHT: To tackle this issue, we investigate to predict the veracity of answers in this paper and introduce AnswerFact, a large scale fact checking dataset from product question answering forums.
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189, TITLE: Context-Aware Answer Extraction in Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.189 AUTHORS: Yeon Seonwoo, Ji-Hoon Kim, Jung-Woo Ha, Alice Oh HIGHLIGHT: To resolve this issue, we propose BLANC (BLock AttentioN for Context prediction) based on two main ideas: context prediction as an auxiliary task in multi-task learning manner, and a block attention method that learns the context prediction task.
190, TITLE: What do Models Learn from Question Answering Datasets? https://www.aclweb.org/anthology/2020.emnlp-main.190 AUTHORS: Priyanka Sen, Amir Saffari HIGHLIGHT: In this paper, we investigate if models are learning reading comprehension from QA datasets by evaluating BERT-based models across five datasets.
191, TITLE: Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading https://www.aclweb.org/anthology/2020.emnlp-main.191 AUTHORS: Yifan Gao, Chien-Sheng Wu, Jingjing Li, Shafiq Joty, Steven C.H. Hoi, Caiming Xiong, Irwin King, Michael Lyu HIGHLIGHT: In this work, we propose "Discern", a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding of both document and dialog.
192, TITLE: A Method for Building a Commonsense Inference Dataset based on Basic Events https://www.aclweb.org/anthology/2020.emnlp-main.192 AUTHORS: Kazumasa Omura, Daisuke Kawahara, Sadao Kurohashi HIGHLIGHT: We present a scalable, low-bias, and low-cost method for building a commonsense inference dataset that combines automatic extraction from a corpus and crowdsourcing.
193, TITLE: Neural Deepfake Detection with Factual Structure of Text https://www.aclweb.org/anthology/2020.emnlp-main.193 AUTHORS: Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin HIGHLIGHT: To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text.
194, TITLE: MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale https://www.aclweb.org/anthology/2020.emnlp-main.194 AUTHORS: Andreas Rücklé, Jonas Pfeiffer, Iryna Gurevych HIGHLIGHT: We propose to incorporate self-supervised with supervised multi-task learning on all available source domains.
195, TITLE: XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques https://www.aclweb.org/anthology/2020.emnlp-main.195 AUTHORS: Rexhina Blloshmi, Rocco Tripodi, Roberto Navigli HIGHLIGHT: In this work we tackle these two problems so as to enable cross-lingual AMR parsing: we explore different transfer learning techniques for producing automatic AMR annotations across languages and develop a cross-lingual AMR parser, XL-AMR.
196, TITLE: Improving AMR Parsing with Sequence-to-Sequence Pre-training https://www.aclweb.org/anthology/2020.emnlp-main.196 AUTHORS: Dongqin Xu, Junhui Li, Muhua Zhu, Min Zhang, Guodong Zhou HIGHLIGHT: In this paper, we focus on sequence-to-sequence (seq2seq) AMR parsing and propose a seq2seq pre-training approach to build pre-trained models in both single and joint way on three relevant tasks, i.e., machine translation, syntactic parsing, and AMR parsing itself.
197, TITLE: Hate-Speech and Offensive Language Detection in Roman Urdu https://www.aclweb.org/anthology/2020.emnlp-main.197 AUTHORS: Hammad Rizwan, Muhammad Haroon Shakeel, Asim Karim HIGHLIGHT: In this study, we: (1) Present a lexicon of hateful words in RU, (2) Develop an annotated dataset called RUHSOLD consisting of 10,012 tweets in RU with both coarse-grained and fine-grained labels of hate-speech and offensive language, (3) Explore the feasibility of transfer learning of five existing embedding models to RU, (4) Propose a novel deep learning architecture called CNN-gram for hate-speech and offensive language detection and compare its performance with seven current baseline approaches on RUHSOLD dataset, and (5) Train domain-specific embeddings on more than 4.7 million tweets and make them publicly available.
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198, TITLE: Suicidal Risk Detection for Military Personnel https://www.aclweb.org/anthology/2020.emnlp-main.198 AUTHORS: Sungjoon Park, Kiwoong Park, Jaimeen Ahn, Alice Oh HIGHLIGHT: We analyze social media for detecting the suicidal risk of military personnel, which is especially crucial for countries with compulsory military service such as the Republic of Korea.
199, TITLE: Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets https://www.aclweb.org/anthology/2020.emnlp-main.199 AUTHORS: Nedjma Ousidhoum, Yangqiu Song, Dit-Yan Yeung HIGHLIGHT: We examine selection bias in hate speech in a language and label independent fashion.
200, TITLE: HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media https://www.aclweb.org/anthology/2020.emnlp-main.200 AUTHORS: Hsin-Yu Chen, Cheng-Te Li HIGHLIGHT: In this paper, therefore, we propose a novel deep model, HEterogeneous Neural Interaction Networks (HENIN), for explainable cyberbullying detection.
201, TITLE: Reactive Supervision: A New Method for Collecting Sarcasm Data https://www.aclweb.org/anthology/2020.emnlp-main.201 AUTHORS: Boaz Shmueli, Lun-Wei Ku, Soumya Ray HIGHLIGHT: We introduce reactive supervision, a novel data collection method that utilizes the dynamics of online conversations to overcome the limitations of existing data collection techniques.
202, TITLE: Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.202 AUTHORS: Dana Ruiter, Josef van Genabith, Cristina España-Bonet HIGHLIGHT: In this study, we provide an in-depth analysis of the sampling choices the SSNMT model makes during training.
203, TITLE: Towards Reasonably-Sized Character-Level Transformer NMT by Finetuning Subword Systems https://www.aclweb.org/anthology/2020.emnlp-main.203 AUTHORS: Jind?ich Libovický, Alexander Fraser HIGHLIGHT: We show that by initially training a subword model and then finetuning it on characters, we can obtain a neural machine translation model that works at the character level without requiring token segmentation.
204, TITLE: Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages https://www.aclweb.org/anthology/2020.emnlp-main.204 AUTHORS: Michael A. Hedderich, David Adelani, Dawei Zhu, Jesujoba Alabi, Udia Markus, Dietrich Klakow HIGHLIGHT: In this work, we study trends in performance for different amounts of available resources for the three African languages Hausa, isiXhosa and on both NER and topic classification.
205, TITLE: Translation Quality Estimation by Jointly Learning to Score and Rank https://www.aclweb.org/anthology/2020.emnlp-main.205 AUTHORS: Jingyi Zhang, Josef van Genabith HIGHLIGHT: In order to make use of different types of human evaluation data for supervised learning, we present a multi- task learning QE model that jointly learns two tasks: score a translation and rank two translations.
206, TITLE: Direct Segmentation Models for Streaming Speech Translation https://www.aclweb.org/anthology/2020.emnlp-main.206 AUTHORS: Javier Iranzo-Sánchez, Adrià Giménez Pastor, Joan Albert Silvestre-Cerdà, Pau Baquero-Arnal, Jorge Civera Saiz, Alfons Juan HIGHLIGHT: This work proposes novel segmentation models for streaming ST that incorporate not only textual, but also acoustic information to decide when the ASR output is split into a chunk.
207, TITLE: Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.207
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AUTHORS: Tahmid Hasan, Abhik Bhattacharjee, Kazi Samin, Masum Hasan, Madhusudan Basak, M. Sohel Rahman, Rifat Shahriyar HIGHLIGHT: In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status.
208, TITLE: CSP:Code-Switching Pre-training for Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.208 AUTHORS: Zhen Yang, Bojie Hu, Ambyera Han, Shen Huang, Qi Ju HIGHLIGHT: This paper proposes a new pre-training method, called Code-Switching Pre-training (CSP for short) for Neural Machine Translation (NMT).
209, TITLE: Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias https://www.aclweb.org/anthology/2020.emnlp-main.209 AUTHORS: Ana Valeria González, Maria Barrett, Rasmus Hvingelby, Kellie Webster, Anders Søgaard HIGHLIGHT: We present a multilingual, multi-task challenge dataset, which spans four languages and four NLP tasks and focuses only on this phenomenon.
210, TITLE: Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information https://www.aclweb.org/anthology/2020.emnlp-main.210 AUTHORS: Zehui Lin, Xiao Pan, Mingxuan Wang, Xipeng Qiu, Jiangtao Feng, Hao Zhou, Lei Li HIGHLIGHT: We propose mRASP, an approach to pre-train a universal multilingual neural machine translation model.
211, TITLE: Losing Heads in the Lottery: Pruning Transformer Attention in Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.211 AUTHORS: Maximiliana Behnke, Kenneth Heafield HIGHLIGHT: In this paper, we apply the lottery ticket hypothesis to prune heads in the early stages of training.
212, TITLE: Towards Enhancing Faithfulness for Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.212 AUTHORS: Rongxiang Weng, Heng Yu, Xiangpeng Wei, Weihua Luo HIGHLIGHT: In this paper, we propose a novel training strategy with a multi-task learning paradigm to build a faithfulness enhanced NMT model (named FEnmt).
213, TITLE: COMET: A Neural Framework for MT Evaluation https://www.aclweb.org/anthology/2020.emnlp-main.213 AUTHORS: Ricardo Rei, Craig Stewart, Ana C Farinha, Alon Lavie HIGHLIGHT: We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art levels of correlation with human judgements.
214, TITLE: Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT https://www.aclweb.org/anthology/2020.emnlp-main.214 AUTHORS: Alexandra Chronopoulou, Dario Stojanovski, Alexander Fraser HIGHLIGHT: We present an effective approach that reuses an LM that is pretrained only on the high-resource language.
215, TITLE: LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space https://www.aclweb.org/anthology/2020.emnlp-main.215 AUTHORS: Tasnim Mohiuddin, M Saiful Bari, Shafiq Joty HIGHLIGHT: In this work, we propose a novel semi-supervised method to learn cross-lingual word embeddings for BLI.
216, TITLE: Uncertainty-Aware Semantic Augmentation for Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.216 AUTHORS: Xiangpeng Wei, Heng Yu, Yue Hu, Rongxiang Weng, Luxi Xing, Weihua Luo HIGHLIGHT: To address this problem, we propose uncertainty-aware semantic augmentation, which explicitly captures the universal semantic information among multiple semantically-equivalent source sentences and enhances the hidden representations with this information for better translations.
217, TITLE: Can Automatic Post-Editing Improve NMT? https://www.aclweb.org/anthology/2020.emnlp-main.217
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AUTHORS: Shamil Chollampatt, Raymond Hendy Susanto, Liling Tan, Ewa Szymanska HIGHLIGHT: We hypothesize that APE models have been underperforming in improving NMT translations due to the lack of adequate supervision. To ascertain our hypothesis, we compile a larger corpus of human post-edits of English to German NMT.
218, TITLE: Parsing Gapping Constructions Based on Grammatical and Semantic Roles https://www.aclweb.org/anthology/2020.emnlp-main.218 AUTHORS: Yoshihide Kato, Shigeki Matsubara HIGHLIGHT: This paper proposes a method of parsing sentences with gapping to recover elided elements.
219, TITLE: Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(n^6) down to O(n^3) https://www.aclweb.org/anthology/2020.emnlp-main.219 AUTHORS: Caio Corro HIGHLIGHT: We introduce a novel chart-based algorithm for span-based parsing of discontinuous constituency trees of block degree two, including ill-nested structures.
220, TITLE: Some Languages Seem Easier to Parse Because Their Treebanks Leak https://www.aclweb.org/anthology/2020.emnlp-main.220 AUTHORS: Anders Søgaard HIGHLIGHT: We compute graph isomorphisms, and show that, treebank size aside, overlap between training and test graphs explain more of the observed variation than standard explanations such as the above.
221, TITLE: Discontinuous Constituent Parsing as Sequence Labeling https://www.aclweb.org/anthology/2020.emnlp-main.221 AUTHORS: David Vilares, Carlos Gómez-Rodríguez HIGHLIGHT: This paper reduces discontinuous parsing to sequence labeling.
222, TITLE: Modularized Syntactic Neural Networks for Sentence Classification https://www.aclweb.org/anthology/2020.emnlp-main.222 AUTHORS: Haiyan Wu, Ying Liu, Shaoyun Shi HIGHLIGHT: This paper focuses on tree-based modeling for the sentence classification task.
223, TITLE: TED-CDB: A Large-Scale Chinese Discourse Relation Dataset on TED Talks https://www.aclweb.org/anthology/2020.emnlp-main.223 AUTHORS: Wanqiu Long, Bonnie Webber, Deyi Xiong HIGHLIGHT: As different genres are known to differ in their communicative properties and as previously, for Chinese, discourse relations have only been annotated over news text, we have created the TED-CDB dataset.
224, TITLE: QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines https://www.aclweb.org/anthology/2020.emnlp-main.224 AUTHORS: Valentina Pyatkin, Ayal Klein, Reut Tsarfaty, Ido Dagan HIGHLIGHT: This paper proposes a novel representation of discourse relations as QA pairs, which in turn allows us to crowd- source wide-coverage data annotated with discourse relations, via an intuitively appealing interface for composing such questions and answers.
225, TITLE: Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays https://www.aclweb.org/anthology/2020.emnlp-main.225 AUTHORS: Wei Song, Ziyao Song, Ruiji Fu, Lizhen Liu, Miaomiao Cheng, Ting Liu HIGHLIGHT: This paper proposes to adapt self-attention to discourse level for modeling discourse elements in argumentative student essays.
226, TITLE: MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models https://www.aclweb.org/anthology/2020.emnlp-main.226 AUTHORS: Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, Bryan Catanzaro HIGHLIGHT: In this paper, we propose MEGATRON-CNTRL, a novel framework that uses large-scale language models and adds control to text generation by incorporating an external knowledge base.
227, TITLE: Incomplete Utterance Rewriting as Semantic Segmentation
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https://www.aclweb.org/anthology/2020.emnlp-main.227 AUTHORS: Qian Liu, Bei Chen, Jian-Guang Lou, Bin Zhou, Dongmei Zhang HIGHLIGHT: In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task.
228, TITLE: Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples https://www.aclweb.org/anthology/2020.emnlp-main.228 AUTHORS: Lihao Wang, Xiaoqing Zheng HIGHLIGHT: We propose a method inspired by adversarial training to generate more meaningful and valuable training examples by continually identifying the weak spots of a model, and to enhance the model by gradually adding the generated adversarial examples to the training set.
229, TITLE: Homophonic Pun Generation with Lexically Constrained Rewriting https://www.aclweb.org/anthology/2020.emnlp-main.229 AUTHORS: Zhiwei Yu, Hongyu Zang, Xiaojun Wan HIGHLIGHT: In this paper, we focus on the task of generating a pun sentence given a pair of homophones.
230, TITLE: How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue https://www.aclweb.org/anthology/2020.emnlp-main.230 AUTHORS: Henry Elder, Alexander O’Connor, Jennifer Foster HIGHLIGHT: To overcome this issue, we propose a data augmentation approach which allows us to restrict the output of a network and guarantee reliability.
231, TITLE: Multilingual AMR-to-Text Generation https://www.aclweb.org/anthology/2020.emnlp-main.231 AUTHORS: Angela Fan, Claire Gardent HIGHLIGHT: In this work, we focus on Abstract Meaning Representations (AMRs) as structured input, where previous research has overwhelmingly focused on generating only into English.
232, TITLE: Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation https://www.aclweb.org/anthology/2020.emnlp-main.232 AUTHORS: Francisco Vargas, Ryan Cotterell HIGHLIGHT: In this work, we generalize their method to a kernelized, non-linear version.
233, TITLE: Lifelong Language Knowledge Distillation https://www.aclweb.org/anthology/2020.emnlp-main.233 AUTHORS: Yung-Sung Chuang, Shang-Yu Su, Yun-Nung Chen HIGHLIGHT: To address this issue, we present Lifelong Language Knowledge Distillation (L2KD), a simple but efficient method that can be easily applied to existing LLL architectures in order to mitigate the degradation.
234, TITLE: Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models https://www.aclweb.org/anthology/2020.emnlp-main.234 AUTHORS: Alexander Terenin, Måns Magnusson, Leif Jonsson HIGHLIGHT: In this work, we study data-parallel training for the hierarchical Dirichlet process (HDP) topic model.
235, TITLE: Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs https://www.aclweb.org/anthology/2020.emnlp-main.235 AUTHORS: Jueqing Lu, Lan Du, Ming Liu, Joanna Dipnall HIGHLIGHT: In this paper, we present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships in order to study how the aggregated knowledge can benefit multi-label zero/few-shot document classification.
236, TITLE: Word Rotator's Distance https://www.aclweb.org/anthology/2020.emnlp-main.236 AUTHORS: Sho Yokoi, Ryo Takahashi, Reina Akama, Jun Suzuki, Kentaro Inui HIGHLIGHT: Accordingly, we propose decoupling word vectors into their norm and direction then computing the alignment- based similarity with the help of earth mover's distance (optimal transport), which we refer to as word rotator's distance.
237, TITLE: Disentangle-based Continual Graph Representation Learning https://www.aclweb.org/anthology/2020.emnlp-main.237 AUTHORS: Xiaoyu Kou, Yankai Lin, Shaobo Liu, Peng Li, Jie Zhou, Yan Zhang
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HIGHLIGHT: To address this issue, we study the problem of continual graph representation learning which aims to continually train a GE model on new data to learn incessantly emerging multi-relational data while avoiding catastrophically forgetting old learned knowledge.
238, TITLE: Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction https://www.aclweb.org/anthology/2020.emnlp-main.238 AUTHORS: Xu Zhao, Zihao Wang, Hao Wu, Yong Zhang HIGHLIGHT: In this paper, we propose a new semi-supervised BLI framework to encourage the interaction between the supervised signal and unsupervised alignment.
239, TITLE: Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains https://www.aclweb.org/anthology/2020.emnlp-main.239 AUTHORS: Weijie Yu, Chen Xu, Jun Xu, Liang Pang, Xiaopeng Gao, Xiaozhao Wang, Ji-Rong Wen HIGHLIGHT: In this paper, we propose a novel match method tailored for text matching in asymmetrical domains, called WD-Match.
240, TITLE: A Simple Approach to Learning Unsupervised Multilingual Embeddings https://www.aclweb.org/anthology/2020.emnlp-main.240 AUTHORS: Pratik Jawanpuria, Mayank Meghwanshi, Bamdev Mishra HIGHLIGHT: In contrast, we propose a simple approach by decoupling the above two sub-problems and solving them separately, one after another, using existing techniques.
241, TITLE: Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games https://www.aclweb.org/anthology/2020.emnlp-main.241 AUTHORS: Subhajit Chaudhury, Daiki Kimura, Kartik Talamadupula, Michiaki Tatsubori, Asim Munawar, Ryuki Tachibana HIGHLIGHT: To address this issue, we propose Context Relevant Episodic State Truncation (CREST) for irrelevant token removal in observation text for improved generalization.
242, TITLE: BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's Distance https://www.aclweb.org/anthology/2020.emnlp-main.242 AUTHORS: Jianquan Li, Xiaokang Liu, Honghong Zhao, Ruifeng Xu, Min Yang, Yaohong Jin HIGHLIGHT: In this paper, we propose a novel BERT distillation method based on many-to-many layer mapping, which allows each intermediate student layer to learn from any intermediate teacher layers.
243, TITLE: Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking https://www.aclweb.org/anthology/2020.emnlp-main.243 AUTHORS: Yexiang Wang, Yi Guo, Siqi Zhu HIGHLIGHT: In this paper, we propose a new architecture to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN.
244, TITLE: Don't Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.244 AUTHORS: Yuxiang Wu, Sebastian Riedel, Pasquale Minervini, Pontus Stenetorp HIGHLIGHT: To reduce this cost, we propose the use of adaptive computation to control the computational budget allocated for the passages to be read.
245, TITLE: Multi-Step Inference for Reasoning Over Paragraphs https://www.aclweb.org/anthology/2020.emnlp-main.245 AUTHORS: Jiangming Liu, Matt Gardner, Shay B. Cohen, Mirella Lapata HIGHLIGHT: We present a middle ground between these two extremes: a compositional model reminiscent of neural module networks that can perform chained logical reasoning.
246, TITLE: Learning a Cost-Effective Annotation Policy for Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.246 AUTHORS: Bernhard Kratzwald, Stefan Feuerriegel, Huan Sun HIGHLIGHT: As a remedy, we propose a novel framework for annotating QA datasets that entails learning a cost-effective annotation policy and a semi-supervised annotation scheme.
247, TITLE: Scene Restoring for Narrative Machine Reading Comprehension
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https://www.aclweb.org/anthology/2020.emnlp-main.247 AUTHORS: Zhixing Tian, Yuanzhe Zhang, Kang Liu, Jun Zhao, Yantao Jia, Zhicheng Sheng HIGHLIGHT: Inspired by this behavior of humans, we propose a method to let the machine imagine a scene during reading narrative for better comprehension.
248, TITLE: A Simple and Effective Model for Answering Multi-span Questions https://www.aclweb.org/anthology/2020.emnlp-main.248 AUTHORS: Elad Segal, Avia Efrat, Mor Shoham, Amir Globerson, Jonathan Berant HIGHLIGHT: In this work, we propose a simple architecture for answering multi-span questions by casting the task as a sequence tagging problem, namely, predicting for each input token whether it should be part of the output or not.
249, TITLE: Top-Rank-Focused Adaptive Vote Collection for the Evaluation of Domain-Specific Semantic Models https://www.aclweb.org/anthology/2020.emnlp-main.249 AUTHORS: Pierangelo Lombardo, Alessio Boiardi, Luca Colombo, Angelo Schiavone, Nicolò Tamagnone HIGHLIGHT: In this work, we give a threefold contribution to address these requirements: (i) we define a protocol for the construction, based on adaptive pairwise comparisons, of a relatedness-based evaluation dataset tailored on the available resources and optimized to be particularly accurate in top-rank evaluation; (ii) we define appropriate metrics, extensions of well-known ranking correlation coefficients, to evaluate a semantic model via the aforementioned dataset by taking into account the greater significance of top ranks.
250, TITLE: Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining https://www.aclweb.org/anthology/2020.emnlp-main.250 AUTHORS: Chengyu Wang, Minghui Qiu, Jun Huang, Xiaofeng He HIGHLIGHT: In this paper, we propose an effective learning procedure named Meta Fine-Tuning (MFT), serving as a meta- learner to solve a group of similar NLP tasks for neural language models.
251, TITLE: Incorporating Behavioral Hypotheses for Query Generation https://www.aclweb.org/anthology/2020.emnlp-main.251 AUTHORS: Ruey-Cheng Chen, Chia-Jung Lee HIGHLIGHT: This paper induces these behavioral biases as hypotheses for query generation, where a generic encoder-decoder Transformer framework is presented to aggregate arbitrary hypotheses of choice.
252, TITLE: Conditional Causal Relationships between Emotions and Causes in Texts https://www.aclweb.org/anthology/2020.emnlp-main.252 AUTHORS: Xinhong Chen, Qing Li, Jianping Wang HIGHLIGHT: To address such an issue, we propose a new task of determining whether or not an input pair of emotion and cause has a valid causal relationship under different contexts, and construct a corresponding dataset via manual annotation and negative sampling based on an existing benchmark dataset.
253, TITLE: COMETA: A Corpus for Medical Entity Linking in the Social Media https://www.aclweb.org/anthology/2020.emnlp-main.253 AUTHORS: Marco Basaldella, Fangyu Liu, Ehsan Shareghi, Nigel Collier HIGHLIGHT: To address this we introduce a new corpus called COMETA, consisting of 20k English biomedical entity mentions from Reddit expert-annotated with links to SNOMED CT, a widely-used medical knowledge graph.
254, TITLE: Pareto Probing: Trading Off Accuracy for Complexity https://www.aclweb.org/anthology/2020.emnlp-main.254 AUTHORS: Tiago Pimentel, Naomi Saphra, Adina Williams, Ryan Cotterell HIGHLIGHT: In our contribution to this discussion, we argue, first, for a probe metric that reflects the trade-off between probe complexity and performance: the Pareto hypervolume.
255, TITLE: Interpretation of NLP models through input marginalization https://www.aclweb.org/anthology/2020.emnlp-main.255 AUTHORS: Siwon Kim, Jihun Yi, Eunji Kim, Sungroh Yoon HIGHLIGHT: In this study, we raise the out-of-distribution problem induced by the existing interpretation methods and present a remedy; we propose to marginalize each token out.
256, TITLE: Generating Label Cohesive and Well-Formed Adversarial Claims https://www.aclweb.org/anthology/2020.emnlp-main.256 AUTHORS: Pepa Atanasova, Dustin Wright, Isabelle Augenstein
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HIGHLIGHT: We extend the HotFlip attack algorithm used for universal trigger generation by jointly minimizing the target class loss of a fact checking model and the entailment class loss of an auxiliary natural language inference model.
257, TITLE: Are All Good Word Vector Spaces Isomorphic? https://www.aclweb.org/anthology/2020.emnlp-main.257 AUTHORS: Ivan Vuli?, Sebastian Ruder, Anders Søgaard HIGHLIGHT: In this work, we ask whether non-isomorphism is also crucially a sign of degenerate word vector spaces.
258, TITLE: Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks https://www.aclweb.org/anthology/2020.emnlp-main.258 AUTHORS: Chengyue Jiang, Yinggong Zhao, Shanbo Chu, Libin Shen, Kewei Tu HIGHLIGHT: In this paper, we propose a type of recurrent neural networks called FA-RNNs that combine the advantages of neural networks and regular expression rules.
259, TITLE: When BERT Plays the Lottery, All Tickets Are Winning https://www.aclweb.org/anthology/2020.emnlp-main.259 AUTHORS: Sai Prasanna, Anna Rogers, Anna Rumshisky HIGHLIGHT: For fine-tuned BERT, we show that (a) it is possible to find subnetworks achieving performance that is comparable with that of the full model, and (b) similarly-sized subnetworks sampled from the rest of the model perform worse.
260, TITLE: On the weak link between importance and prunability of attention heads https://www.aclweb.org/anthology/2020.emnlp-main.260 AUTHORS: Aakriti Budhraja, Madhura Pande, Preksha Nema, Pratyush Kumar, Mitesh M. Khapra HIGHLIGHT: Given the success of Transformer-based models, two directions of study have emerged: interpreting role of individual attention heads and down-sizing the models for efficiency. Our work straddles these two streams: We analyse the importance of basing pruning strategies on the interpreted role of the attention heads.
261, TITLE: Towards Interpreting BERT for Reading Comprehension Based QA https://www.aclweb.org/anthology/2020.emnlp-main.261 AUTHORS: Sahana Ramnath, Preksha Nema, Deep Sahni, Mitesh M. Khapra HIGHLIGHT: In this work, we attempt to interpret BERT for RCQA.
262, TITLE: How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking https://www.aclweb.org/anthology/2020.emnlp-main.262 AUTHORS: Nicola De Cao, Michael Sejr Schlichtkrull, Wilker Aziz, Ivan Titov HIGHLIGHT: To deal with these challenges, we introduce Differentiable Masking.
263, TITLE: A Diagnostic Study of Explainability Techniques for Text Classification https://www.aclweb.org/anthology/2020.emnlp-main.263 AUTHORS: Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, Isabelle Augenstein HIGHLIGHT: In this paper, we develop a comprehensive list of diagnostic properties for evaluating existing explainability techniques.
264, TITLE: STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.264 AUTHORS: Hrituraj Singh, Sumit Shekhar HIGHLIGHT: We propose STL-CQA which improves the question/answering through sequential elements localization, question encoding and then, a structural transformer-based learning approach.
265, TITLE: Learning to Contrast the Counterfactual Samples for Robust Visual Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.265 AUTHORS: Zujie Liang, Weitao Jiang, Haifeng Hu, Jiaying Zhu HIGHLIGHT: Therefore, we introduce a novel self-supervised contrastive learning mechanism to learn the relationship between original samples, factual samples and counterfactual samples.
266, TITLE: Learning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization https://www.aclweb.org/anthology/2020.emnlp-main.266 AUTHORS: Zhenjie Zhao, Evangelos Papalexakis, Xiaojuan Ma
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HIGHLIGHT: In this paper, we formulate physical commonsense learning as a knowledge graph completion problem to better use the latent relationships among training samples.
267, TITLE: A Visually-grounded First-person Dialogue Dataset with Verbal and Non-verbal Responses https://www.aclweb.org/anthology/2020.emnlp-main.267 AUTHORS: Hisashi Kamezawa, Noriki Nishida, Nobuyuki Shimizu, Takashi Miyazaki, Hideki Nakayama HIGHLIGHT: In this paper, we propose a visually-grounded first-person dialogue (VFD) dataset with verbal and non-verbal responses.
268, TITLE: Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings https://www.aclweb.org/anthology/2020.emnlp-main.268 AUTHORS: Yue Wang, Jing Li, Michael Lyu, Irwin King HIGHLIGHT: In this work, we explore the joint effects of texts and images in predicting the keyphrases for a multimedia post.
269, TITLE: VD-BERT: A Unified Vision and Dialog Transformer with BERT https://www.aclweb.org/anthology/2020.emnlp-main.269 AUTHORS: Yue Wang, Shafiq Joty, Michael Lyu, Irwin King, Caiming Xiong, Steven C.H. Hoi HIGHLIGHT: By contrast, in this work, we propose VD-BERT, a simple yet effective framework of unified vision-dialog Transformer that leverages the pretrained BERT language models for Visual Dialog tasks.
270, TITLE: The Grammar of Emergent Languages https://www.aclweb.org/anthology/2020.emnlp-main.270 AUTHORS: Oskar van der Wal, Silvan de Boer, Elia Bruni, Dieuwke Hupkes HIGHLIGHT: In this paper, we consider the syntactic properties of languages emerged in referential games, using unsupervised grammar induction (UGI) techniques originally designed to analyse natural language.
271, TITLE: Sub-Instruction Aware Vision-and-Language Navigation https://www.aclweb.org/anthology/2020.emnlp-main.271 AUTHORS: Yicong Hong, Cristian Rodriguez, Qi Wu, Stephen Gould HIGHLIGHT: In this work, we focus on the granularity of the visual and language sequences as well as the traceability of agents through the completion of an instruction.
272, TITLE: Knowledge-Grounded Dialogue Generation with Pre-trained Language Models https://www.aclweb.org/anthology/2020.emnlp-main.272 AUTHORS: Xueliang Zhao, Wei Wu, Can Xu, Chongyang Tao, Dongyan Zhao, Rui Yan HIGHLIGHT: To leverage the redundant external knowledge under capacity constraint, we propose equipping response generation defined by a pre-trained language model with a knowledge selection module, and an unsupervised approach to jointly optimizing knowledge selection and response generation with unlabeled dialogues.
273, TITLE: MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems https://www.aclweb.org/anthology/2020.emnlp-main.273 AUTHORS: Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, Pascale Fung HIGHLIGHT: In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task- oriented dialogue systems and alleviate the over-dependency on annotated data.
274, TITLE: Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation https://www.aclweb.org/anthology/2020.emnlp-main.274 AUTHORS: Kang Min Yoo, Hanbit Lee, Franck Dernoncourt, Trung Bui, Walter Chang, Sang-goo Lee HIGHLIGHT: In this work, we extend this approach to the task of dialog state tracking for goaloriented dialogs.
275, TITLE: Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation https://www.aclweb.org/anthology/2020.emnlp-main.275 AUTHORS: Xiuyi Chen, Fandong Meng, Peng Li, Feilong Chen, Shuang Xu, Bo Xu, Jie Zhou HIGHLIGHT: Here, we deal with these issues on two aspects: (1) We enhance the prior selection module with the necessary posterior information obtained from the specially designed Posterior Information Prediction Module (PIPM); (2) We propose a Knowledge Distillation Based Training Strategy (KDBTS) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection.
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276, TITLE: Counterfactual Off-Policy Training for Neural Dialogue Generation https://www.aclweb.org/anthology/2020.emnlp-main.276 AUTHORS: Qingfu Zhu, Wei-Nan Zhang, Ting Liu, William Yang Wang HIGHLIGHT: In this paper, we propose to explore potential responses by counterfactual reasoning.
277, TITLE: Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data https://www.aclweb.org/anthology/2020.emnlp-main.277 AUTHORS: Rongsheng Zhang, Yinhe Zheng, Jianzhi Shao, Xiaoxi Mao, Yadong Xi, Minlie Huang HIGHLIGHT: To address this data dilemma, we propose a novel data augmentation method for training open-domain dialogue models by utilizing unpaired data.
278, TITLE: Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network https://www.aclweb.org/anthology/2020.emnlp-main.278 AUTHORS: Sihan Wang, Kaijie Zhou, Kunfeng Lai, Jianping Shen HIGHLIGHT: We introduce a framework of Monte Carlo Tree Search with Double-q Dueling network (MCTS-DDU) for task-completion dialogue policy learning.
279, TITLE: Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks https://www.aclweb.org/anthology/2020.emnlp-main.279 AUTHORS: Yufan Zhao, Can Xu, Wei Wu HIGHLIGHT: In this work, we pursue a model that has a simple structure yet can effectively leverage conversation contexts for response generation.
280, TITLE: AttnIO: Knowledge Graph Exploration with In-and-Out Attention Flow for Knowledge-Grounded Dialogue https://www.aclweb.org/anthology/2020.emnlp-main.280 AUTHORS: Jaehun Jung, Bokyung Son, Sungwon Lyu HIGHLIGHT: To this effect, we present AttnIO, a new dialog-conditioned path traversal model that makes a full use of rich structural information in KG based on two directions of attention flows.
281, TITLE: Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training https://www.aclweb.org/anthology/2020.emnlp-main.281 AUTHORS: Wanwei He, Min Yang, Rui Yan, Chengming Li, Ying Shen, Ruifeng Xu HIGHLIGHT: In this paper, we propose a "Two-Teacher One-Student" learning framework (TTOS) for task-oriented dialogue, with the goal of retrieving accurate KB entities and generating human-like responses simultaneously.
282, TITLE: Task-oriented Domain-specific Meta-Embedding for Text Classification https://www.aclweb.org/anthology/2020.emnlp-main.282 AUTHORS: Xin Wu, Yi Cai, Yang Kai, Tao Wang, Qing Li HIGHLIGHT: In this paper, we propose a method to incorporate both domain-specific and task-oriented information into meta-embeddings.
283, TITLE: Don't Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation https://www.aclweb.org/anthology/2020.emnlp-main.283 AUTHORS: Daniel Loureiro, Jose Camacho-Collados HIGHLIGHT: In this paper, we propose a simple method to provide annotations for most unambiguous words in a large corpus.
284, TITLE: Within-Between Lexical Relation Classification https://www.aclweb.org/anthology/2020.emnlp-main.284 AUTHORS: Oren Barkan, Avi Caciularu, Ido Dagan HIGHLIGHT: We propose the novel \textit{Within-Between} Relation model for recognizing lexical-semantic relations between words.
285, TITLE: With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation https://www.aclweb.org/anthology/2020.emnlp-main.285 AUTHORS: Bianca Scarlini, Tommaso Pasini, Roberto Navigli HIGHLIGHT: In this paper we present ARES (context-AwaRe Embeddings of Senses), a semi-supervised approach to producing sense embeddings for the lexical meanings within a lexical knowledge base that lie in a space that is comparable to that of contextualized word vectors.
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286, TITLE: Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis https://www.aclweb.org/anthology/2020.emnlp-main.286 AUTHORS: Mi Zhang, Tieyun Qian HIGHLIGHT: To tackle the above two limitations, we propose a novel architecture which convolutes over hierarchical syntactic and lexical graphs.
287, TITLE: Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis https://www.aclweb.org/anthology/2020.emnlp-main.287 AUTHORS: Yuncong Li, Cunxiang Yin, Sheng-hua Zhong, Xu Pan HIGHLIGHT: In this paper, we propose a Multi-Instance Multi-Label Learning Network for Aspect-Category sentiment analysis (AC-MIMLLN), which treats sentences as bags, words as instances, and the words indicating an aspect category as the key instances of the aspect category.
288, TITLE: Aspect Sentiment Classification with Aspect-Specific Opinion Spans https://www.aclweb.org/anthology/2020.emnlp-main.288 AUTHORS: Lu Xu, Lidong Bing, Wei Lu, Fei Huang HIGHLIGHT: In this paper, we present a neat and effective structured attention model by aggregating multiple linear-chain CRFs.
289, TITLE: Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme https://www.aclweb.org/anthology/2020.emnlp-main.289 AUTHORS: Chaofa Yuan, Chuang Fan, Jianzhu Bao, Ruifeng Xu HIGHLIGHT: Targeting this issue, we regard the task as a sequence labeling problem and propose a novel tagging scheme with coding the distance between linked components into the tags, so that emotions and the corresponding causes can be extracted simultaneously.
290, TITLE: End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning https://www.aclweb.org/anthology/2020.emnlp-main.290 AUTHORS: Zixiang Ding, Rui Xia, Jianfei Yu HIGHLIGHT: To tackle these shortcomings, we propose two joint frameworks for ECPE: 1) multi-label learning for the extraction of the cause clauses corresponding to the specified emotion clause (CMLL) and 2) multi-label learning for the extraction of the emotion clauses corresponding to the specified cause clause (EMLL).
291, TITLE: Multi-modal Multi-label Emotion Detection with Modality and Label Dependence https://www.aclweb.org/anthology/2020.emnlp-main.291 AUTHORS: Dong Zhang, Xincheng Ju, Junhui Li, Shoushan Li, Qiaoming Zhu, Guodong Zhou HIGHLIGHT: In this paper, we focus on multi-label emotion detection in a multi-modal scenario.
292, TITLE: Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis https://www.aclweb.org/anthology/2020.emnlp-main.292 AUTHORS: Xiaoyu Xing, Zhijing Jin, Di Jin, Bingning Wang, Qi Zhang, Xuanjing Huang HIGHLIGHT: To solve this problem, we develop a simple but effective approach to enrich ABSA test sets.
293, TITLE: Modeling Content Importance for Summarization with Pre-trained Language Models https://www.aclweb.org/anthology/2020.emnlp-main.293 AUTHORS: Liqiang Xiao, Lu Wang, Hao He, Yaohui Jin HIGHLIGHT: In this work, we apply information theory on top of pre-trained language models and define the concept of importance from the perspective of information amount.
294, TITLE: Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning https://www.aclweb.org/anthology/2020.emnlp-main.294 AUTHORS: Hanlu Wu, Tengfei Ma, Lingfei Wu, Tariro Manyumwa, Shouling Ji HIGHLIGHT: In this work, we propose to evaluate the summary qualities without reference summaries by unsupervised contrastive learning.
295, TITLE: Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network https://www.aclweb.org/anthology/2020.emnlp-main.295 AUTHORS: Ruipeng Jia, Yanan Cao, Hengzhu Tang, Fang Fang, Cong Cao, Shi Wang
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HIGHLIGHT: In this paper, we propose HAHSum (as shorthand for Hierarchical Attentive Heterogeneous Graph for Text Summarization), which well models different levels of information, including words and sentences, and spotlights redundancy dependencies between sentences.
296, TITLE: Coarse-to-Fine Query Focused Multi-Document Summarization https://www.aclweb.org/anthology/2020.emnlp-main.296 AUTHORS: Yumo Xu, Mirella Lapata HIGHLIGHT: We propose a coarse-to-fine modeling framework which employs progressively more accurate modules for estimating whether text segments are relevant, likely to contain an answer, and central.
297, TITLE: Pre-training for Abstractive Document Summarization by Reinstating Source Text https://www.aclweb.org/anthology/2020.emnlp-main.297 AUTHORS: Yanyan Zou, Xingxing Zhang, Wei Lu, Furu Wei, Ming Zhou HIGHLIGHT: This paper presents three sequence-to-sequence pre-training (in shorthand, STEP) objectives which allow us to pre-train a SEQ2SEQ based abstractive summarization model on unlabeled text.
298, TITLE: Learning from Context or Names? An Empirical Study on Neural Relation Extraction https://www.aclweb.org/anthology/2020.emnlp-main.298 AUTHORS: Hao Peng, Tianyu Gao, Xu Han, Yankai Lin, Peng Li, Zhiyuan Liu, Maosong Sun, Jie Zhou HIGHLIGHT: Based on the analyses, we propose an entity-masked contrastive pre-training framework for RE to gain a deeper understanding on both textual context and type information while avoiding rote memorization of entities or use of superficial cues in mentions.
299, TITLE: SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction https://www.aclweb.org/anthology/2020.emnlp-main.299 AUTHORS: Xuming Hu, Lijie Wen, Yusong Xu, Chenwei Zhang, Philip Yu HIGHLIGHT: In this work, we propose a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification.
300, TITLE: Denoising Relation Extraction from Document-level Distant Supervision https://www.aclweb.org/anthology/2020.emnlp-main.300 AUTHORS: Chaojun Xiao, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Maosong Sun, Fen Lin, Leyu Lin HIGHLIGHT: To alleviate this issue, we propose a novel pre-trained model for DocRE, which de-emphasize noisy DS data via multiple pre-training tasks.
301, TITLE: Let's Stop Incorrect Comparisons in End-to-end Relation Extraction! https://www.aclweb.org/anthology/2020.emnlp-main.301 AUTHORS: Bruno Taillé, Vincent Guigue, Geoffrey Scoutheeten, Patrick Gallinari HIGHLIGHT: In this paper, we first identify several patterns of invalid comparisons in published papers and describe them to avoid their propagation. We then propose a small empirical study to quantify the most common mistake’s impact and evaluate it leads to overestimating the final RE performance by around 5% on ACE05.
302, TITLE: Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data https://www.aclweb.org/anthology/2020.emnlp-main.302 AUTHORS: Shachar Rosenman, Alon Jacovi, Yoav Goldberg HIGHLIGHT: We identify failure modes of SOTA relation extraction (RE) models trained on TACRED, which we attribute to limitations in the data annotation process.
303, TITLE: Global-to-Local Neural Networks for Document-Level Relation Extraction https://www.aclweb.org/anthology/2020.emnlp-main.303 AUTHORS: Difeng Wang, Wei Hu, Ermei Cao, Weijian Sun HIGHLIGHT: In this paper, we propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations.
304, TITLE: Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations https://www.aclweb.org/anthology/2020.emnlp-main.304 AUTHORS: Kai Sun, Richong Zhang, Samuel Mensah, Yongyi Mao, Xudong Liu HIGHLIGHT: As a solution, we design a multi-task learning model which we refer to as recurrent interaction network which allows the learning of interactions dynamically, to effectively model task-specific features for classification.
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305, TITLE: Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols https://www.aclweb.org/anthology/2020.emnlp-main.305 AUTHORS: Prachi Jain, Sushant Rathi, Mausam, Soumen Chakrabarti HIGHLIGHT: In response, we propose improved TKBC evaluation protocols for both link and time prediction tasks, dealing with subtle issues that arise from the partial overlap of time intervals in gold instances and system predictions.
306, TITLE: OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction https://www.aclweb.org/anthology/2020.emnlp-main.306 AUTHORS: Keshav Kolluru, Vaibhav Adlakha, Samarth Aggarwal, Mausam, Soumen Chakrabarti HIGHLIGHT: In this paper, we bridge this trade-off by presenting an iterative labeling-based system that establishes a new state of the art for OpenIE, while extracting 10x faster.
307, TITLE: Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder https://www.aclweb.org/anthology/2020.emnlp-main.307 AUTHORS: Wenyue Zhang, Xiaoli Li, Yang Li, Suge Wang, Deyu Li, Jian Liao, Jianxing Zheng HIGHLIGHT: In this paper, we focus on distribution learning by proposing a novel Hierarchical Variational Auto-Encoder (HVAE) model to learn better distribution representation, and design a new drift measure to directly evaluate distribution changes between historical data and new data.
308, TITLE: Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model https://www.aclweb.org/anthology/2020.emnlp-main.308 AUTHORS: Bugeun Kim, Kyung Seo Ki, Donggeon Lee, Gahgene Gweon HIGHLIGHT: To address each of these two issues, we propose a pure neural model, Expression-Pointer Transformer (EPT), which uses (1) Expression' token and (2) operand-context pointers when generating solution equations.
309, TITLE: Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems https://www.aclweb.org/anthology/2020.emnlp-main.309 AUTHORS: Jinghui Qin, Lihui Lin, Xiaodan Liang, Rumin Zhang, Liang Lin HIGHLIGHT: Herein, we propose a simple but efficient method called Universal Expression Tree (UET) to make the first attempt to represent the equations of various MWPs uniformly.
310, TITLE: Neural Topic Modeling by Incorporating Document Relationship Graph https://www.aclweb.org/anthology/2020.emnlp-main.310 AUTHORS: Deyu Zhou, Xuemeng Hu, Rui Wang HIGHLIGHT: In this paper, we propose Graph Topic Model (GTM), a GNN based neural topic model that represents a corpus as a document relationship graph.
311, TITLE: Routing Enforced Generative Model for Recipe Generation https://www.aclweb.org/anthology/2020.emnlp-main.311 AUTHORS: Zhiwei Yu, Hongyu Zang, Xiaojun Wan HIGHLIGHT: In this work, we propose a routing method to dive into the content selection under the internal restrictions.
312, TITLE: Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent https://www.aclweb.org/anthology/2020.emnlp-main.312 AUTHORS: Yun-Hsuan Jen, Chieh-Yang Huang, MeiHua Chen, Ting-Hao Huang, Lun-Wei Ku HIGHLIGHT: Thus, we propose the inference-based learner-like agent to mimic learner behavior and identify good learning materials by examining the agent's performance.
313, TITLE: Selection and Generation: Learning towards Multi-Product Advertisement Post Generation https://www.aclweb.org/anthology/2020.emnlp-main.313 AUTHORS: Zhangming Chan, Yuchi Zhang, Xiuying Chen, Shen Gao, Zhiqiang Zhang, Dongyan Zhao, Rui Yan HIGHLIGHT: We propose a novel end-to-end model named S-MG Net to generate the AD post.
314, TITLE: Form2Seq : A Framework for Higher-Order Form Structure Extraction https://www.aclweb.org/anthology/2020.emnlp-main.314 AUTHORS: Milan Aggarwal, Hiresh Gupta, Mausoom Sarkar, Balaji Krishnamurthy HIGHLIGHT: To mitigate this, we propose Form2Seq, a novel sequence-to-sequence (Seq2Seq) inspired framework for structure extraction using text, with a specific focus on forms, which leverages relative spatial arrangement of structures.
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315, TITLE: Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble https://www.aclweb.org/anthology/2020.emnlp-main.315 AUTHORS: Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong HIGHLIGHT: We propose a filter-and-refine solution based on the stacked-ensemble learning paradigm to address this black- box limitation.
316, TITLE: DagoBERT: Generating Derivational Morphology with a Pretrained Language Model https://www.aclweb.org/anthology/2020.emnlp-main.316 AUTHORS: Valentin Hofmann, Janet Pierrehumbert, Hinrich Schütze HIGHLIGHT: We present the first study investigating this question, taking BERT as the example PLM.
317, TITLE: Attention Is All You Need for Chinese Word Segmentation https://www.aclweb.org/anthology/2020.emnlp-main.317 AUTHORS: Sufeng Duan, Hai Zhao HIGHLIGHT: Taking greedy decoding algorithm as it should be, this work focuses on further strengthening the model itself for Chinese word segmentation (CWS), which results in an even more fast and more accurate CWS model.
318, TITLE: A Joint Multiple Criteria Model in Transfer Learning for Cross-domain Chinese Word Segmentation https://www.aclweb.org/anthology/2020.emnlp-main.318 AUTHORS: Kaiyu Huang, Degen Huang, Zhuang Liu, Fengran Mo HIGHLIGHT: To this end, we propose a joint multiple criteria model that shares all parameters to integrate different segmentation criteria into one model.
319, TITLE: Alignment-free Cross-lingual Semantic Role Labeling https://www.aclweb.org/anthology/2020.emnlp-main.319 AUTHORS: Rui Cai, Mirella Lapata HIGHLIGHT: We propose a cross-lingual SRL model which only requires annotations in a source language and access to raw text in the form of a parallel corpus.
320, TITLE: Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection https://www.aclweb.org/anthology/2020.emnlp-main.320 AUTHORS: Ruize Wang, Duyu Tang, Nan Duan, Wanjun Zhong, Zhongyu Wei, Xuanjing Huang, Daxin Jiang, Ming Zhou HIGHLIGHT: Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques.
321, TITLE: X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset https://www.aclweb.org/anthology/2020.emnlp-main.321 AUTHORS: Angel Daza, Anette Frank HIGHLIGHT: In this work we propose a method to automatically construct an SRL corpus that is parallel in four languages: English, French, German, Spanish, with unified predicate and role annotations that are fully comparable across languages.
322, TITLE: Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling https://www.aclweb.org/anthology/2020.emnlp-main.322 AUTHORS: Diego Marcheggiani, Ivan Titov HIGHLIGHT: In contrast, we show how graph convolutional networks (GCNs) can be used to encode constituent structures and inform an SRL system.
323, TITLE: Fast semantic parsing with well-typedness guarantees https://www.aclweb.org/anthology/2020.emnlp-main.323 AUTHORS: Matthias Lindemann, Jonas Groschwitz, Alexander Koller HIGHLIGHT: We describe an A* parser and a transition-based parser for AM dependency parsing which guarantee well- typedness and improve parsing speed by up to 3 orders of magnitude, while maintaining or improving accuracy.
324, TITLE: Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes https://www.aclweb.org/anthology/2020.emnlp-main.324 AUTHORS: Paulo Cavalin, Victor Henrique Alves Ribeiro, Ana Appel, Claudio Pinhanez HIGHLIGHT: This paper explores how intent classification can be improved by representing the class labels not as a discrete set of symbols but as a space where the word graphs associated to each class are mapped using typical graph embedding techniques.
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325, TITLE: Supervised Seeded Iterated Learning for Interactive Language Learning https://www.aclweb.org/anthology/2020.emnlp-main.325 AUTHORS: Yuchen Lu, Soumye Singhal, Florian Strub, Olivier Pietquin, Aaron Courville HIGHLIGHT: Given these observations, we introduce Supervised Seeded Iterated Learning (SSIL) to combine both methods to minimize their respective weaknesses.
326, TITLE: Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems https://www.aclweb.org/anthology/2020.emnlp-main.326 AUTHORS: Jan Deriu, Don Tuggener, Pius von Däniken, Jon Ander Campos, Alvaro Rodrigo, Thiziri Belkacem, Aitor Soroa, Eneko Agirre, Mark Cieliebak HIGHLIGHT: In this work, we introduce Spot The Bot, a cost-efficient and robust evaluation framework that replaces human- bot conversations with conversations between bots.
327, TITLE: Human-centric dialog training via offline reinforcement learning https://www.aclweb.org/anthology/2020.emnlp-main.327 AUTHORS: Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard HIGHLIGHT: We solve the challenge by developing a novel class of offline RL algorithms. These algorithms use KL-control to penalize divergence from a pre-trained prior language model, and use a new strategy to make the algorithm pessimistic, instead of optimistic, in the face of uncertainty.
328, TITLE: Speakers Fill Lexical Semantic Gaps with Context https://www.aclweb.org/anthology/2020.emnlp-main.328 AUTHORS: Tiago Pimentel, Rowan Hall Maudslay, Damian Blasi, Ryan Cotterell HIGHLIGHT: To investigate whether this is the case, we operationalise the lexical ambiguity of a word as the entropy of meanings it can take, and provide two ways to estimate this-one which requires human annotation (using WordNet), and one which does not (using BERT), making it readily applicable to a large number of languages.
329, TITLE: Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model https://www.aclweb.org/anthology/2020.emnlp-main.329 AUTHORS: Jun Yen Leung, Guy Emerson, Ryan Cotterell HIGHLIGHT: We present the first purely corpus-driven model of multi-lingual adjective ordering in the form of a latent- variable model that can accurately order adjectives across 24 different languages, even when the training and testing languages are different.
330, TITLE: Surprisal Predicts Code-Switching in Chinese-English Bilingual Text https://www.aclweb.org/anthology/2020.emnlp-main.330 AUTHORS: Jesús Calvillo, Le Fang, Jeremy Cole, David Reitter HIGHLIGHT: We describe and model a new dataset of Chinese-English text with 1476 clean code-switched sentences, translated back into Chinese.
331, TITLE: Word Frequency Does Not Predict Grammatical Knowledge in Language Models https://www.aclweb.org/anthology/2020.emnlp-main.331 AUTHORS: Charles Yu, Ryan Sie, Nicolas Tedeschi, Leon Bergen HIGHLIGHT: In this work, we investigate whether there are systematic sources of variation in the language models' accuracy.
332, TITLE: Improving Word Sense Disambiguation with Translations https://www.aclweb.org/anthology/2020.emnlp-main.332 AUTHORS: Yixing Luan, Bradley Hauer, Lili Mou, Grzegorz Kondrak HIGHLIGHT: In this paper, we present a novel approach that improves the performance of a base WSD system using machine translation.
333, TITLE: Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors https://www.aclweb.org/anthology/2020.emnlp-main.333 AUTHORS: Qianchu Liu, Diana McCarthy, Anna Korhonen HIGHLIGHT: In this paper, we present a post-processing technique that enhances these representations by learning a transformation through static anchors.
334, TITLE: Compositional Demographic Word Embeddings
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https://www.aclweb.org/anthology/2020.emnlp-main.334 AUTHORS: Charles Welch, Jonathan K. Kummerfeld, Verónica Pérez-Rosas, Rada Mihalcea HIGHLIGHT: We propose a new form of personalized word embeddings that use demographic-specific word representations derived compositionally from full or partial demographic information for a user (i.e., gender, age, location, religion).
335, TITLE: Do ``Undocumented Workers'' == ``Illegal Aliens''? Differentiating Denotation and Connotation in Vector Spaces https://www.aclweb.org/anthology/2020.emnlp-main.335 AUTHORS: Albert Webson, Zhizhong Chen, Carsten Eickhoff, Ellie Pavlick HIGHLIGHT: In this study, we propose an adversarial nerual netowrk that decomposes a pretrained representation as independent denotation and connotation representations.
336, TITLE: Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization https://www.aclweb.org/anthology/2020.emnlp-main.336 AUTHORS: Jiaao Chen, Diyi Yang HIGHLIGHT: This work proposes a multi-view sequence-to-sequence model by first extracting conversational structures of unstructured daily chats from different views to represent conversations and then utilizing a multi-view decoder to incorporate different views to generate dialogue summaries.
337, TITLE: Few-Shot Learning for Opinion Summarization https://www.aclweb.org/anthology/2020.emnlp-main.337 AUTHORS: Arthur Bražinskas, Mirella Lapata, Ivan Titov HIGHLIGHT: In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text with all expected properties, such as writing style, informativeness, fluency, and sentiment preservation.
338, TITLE: Learning to Fuse Sentences with Transformers for Summarization https://www.aclweb.org/anthology/2020.emnlp-main.338 AUTHORS: Logan Lebanoff, Franck Dernoncourt, Doo Soon Kim, Lidan Wang, Walter Chang, Fei Liu HIGHLIGHT: In this paper, we explore the ability of Transformers to fuse sentences and propose novel algorithms to enhance their ability to perform sentence fusion by leveraging the knowledge of points of correspondence between sentences.
339, TITLE: Stepwise Extractive Summarization and Planning with Structured Transformers https://www.aclweb.org/anthology/2020.emnlp-main.339 AUTHORS: Shashi Narayan, Joshua Maynez, Jakub Adamek, Daniele Pighin, Blaz Bratanic, Ryan McDonald HIGHLIGHT: We propose encoder-centric stepwise models for extractive summarization using structured transformers - HiBERT and Extended Transformers.
340, TITLE: CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval https://www.aclweb.org/anthology/2020.emnlp-main.340 AUTHORS: Shuo Sun, Kevin Duh HIGHLIGHT: We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia.
341, TITLE: SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search https://www.aclweb.org/anthology/2020.emnlp-main.341 AUTHORS: Sean MacAvaney, Arman Cohan, Nazli Goharian HIGHLIGHT: In this work, we present a zero-shot ranking algorithm that adapts to COVID-related scientific literature.
342, TITLE: Modularized Transfomer-based Ranking Framework https://www.aclweb.org/anthology/2020.emnlp-main.342 AUTHORS: Luyu Gao, Zhuyun Dai, Jamie Callan HIGHLIGHT: In this work, we modularize the Transformer ranker into separate modules for text representation and interaction.
343, TITLE: Ad-hoc Document Retrieval using Weak-Supervision with BERT and GPT2 https://www.aclweb.org/anthology/2020.emnlp-main.343 AUTHORS: Yosi Mass, Haggai Roitman HIGHLIGHT: We describe a weakly-supervised method for training deep learning models for the task of ad-hoc document retrieval.
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344, TITLE: Adversarial Semantic Collisions https://www.aclweb.org/anthology/2020.emnlp-main.344 AUTHORS: Congzheng Song, Alexander Rush, Vitaly Shmatikov HIGHLIGHT: We develop gradient-based approaches for generating semantic collisions and demonstrate that state-of-the-art models for many tasks which rely on analyzing the meaning and similarity of texts-including paraphrase identification, document retrieval, response suggestion, and extractive summarization-are vulnerable to semantic collisions.
345, TITLE: Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification https://www.aclweb.org/anthology/2020.emnlp-main.345 AUTHORS: Prithviraj Sen, Marina Danilevsky, Yunyao Li, Siddhartha Brahma, Matthias Boehm, Laura Chiticariu, Rajasekar Krishnamurthy HIGHLIGHT: We present RuleNN, a neural network architecture for learning transparent models for sentence classification.
346, TITLE: AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts https://www.aclweb.org/anthology/2020.emnlp-main.346 AUTHORS: Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, Sameer Singh HIGHLIGHT: To address this, we develop AutoPrompt, an automated method to create prompts for a diverse set of tasks, based on a gradient-guided search.
347, TITLE: Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers https://www.aclweb.org/anthology/2020.emnlp-main.347 AUTHORS: Hanjie Chen, Yangfeng Ji HIGHLIGHT: To address this limitation, we propose the variational word mask (VMASK) method to automatically learn task- specific important words and reduce irrelevant information on classification, which ultimately improves the interpretability of model predictions.
348, TITLE: Sparse Text Generation https://www.aclweb.org/anthology/2020.emnlp-main.348 AUTHORS: Pedro Henrique Martins, Zita Marinho, André F. T. Martins HIGHLIGHT: In this paper, we use the recently introduced entmax transformation to train and sample from a natively sparse language model, avoiding this mismatch.
349, TITLE: PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking https://www.aclweb.org/anthology/2020.emnlp-main.349 AUTHORS: Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, Jianfeng Gao HIGHLIGHT: We present PlotMachines, a neural narrative model that learns to transform an outline into a coherent story by tracking the dynamic plot states.
350, TITLE: Do sequence-to-sequence VAEs learn global features of sentences? https://www.aclweb.org/anthology/2020.emnlp-main.350 AUTHORS: Tom Bosc, Pascal Vincent HIGHLIGHT: To alleviate this, we investigate alternative architectures based on bag-of-words assumptions and language model pretraining.
351, TITLE: Content Planning for Neural Story Generation with Aristotelian Rescoring https://www.aclweb.org/anthology/2020.emnlp-main.351 AUTHORS: Seraphina Goldfarb-Tarrant, Tuhin Chakrabarty, Ralph Weischedel, Nanyun Peng HIGHLIGHT: We utilize a plot-generation language model along with an ensemble of rescoring models that each implement an aspect of good story-writing as detailed in Aristotle's Poetics.
352, TITLE: Generating Dialogue Responses from a Semantic Latent Space https://www.aclweb.org/anthology/2020.emnlp-main.352 AUTHORS: Wei-Jen Ko, Avik Ray, Yilin Shen, Hongxia Jin HIGHLIGHT: In this work, we hypothesize that the current models are unable to integrate information from multiple semantically similar valid responses of a prompt, resulting in the generation of generic and uninformative responses.
353, TITLE: Refer, Reuse, Reduce: Generating Subsequent References in Visual and Conversational Contexts https://www.aclweb.org/anthology/2020.emnlp-main.353
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AUTHORS: Ece Takmaz, Mario Giulianelli, Sandro Pezzelle, Arabella Sinclair, Raquel Fernández HIGHLIGHT: We propose a generation model that produces referring utterances grounded in both the visual and the conversational context.
354, TITLE: Visually Grounded Compound PCFGs https://www.aclweb.org/anthology/2020.emnlp-main.354 AUTHORS: Yanpeng Zhao, Ivan Titov HIGHLIGHT: In this work, we study visually grounded grammar induction and learn a constituency parser from both unlabeled text and its visual groundings.
355, TITLE: ALICE: Active Learning with Contrastive Natural Language Explanations https://www.aclweb.org/anthology/2020.emnlp-main.355 AUTHORS: Weixin Liang, James Zou, Zhou Yu HIGHLIGHT: We propose Active Learning with Contrastive Explanations (ALICE), an expert-in-the-loop training framework that utilizes contrastive natural language explanations to improve data efficiency in learning.
356, TITLE: Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding https://www.aclweb.org/anthology/2020.emnlp-main.356 AUTHORS: Alexander Ku, Peter Anderson, Roma Patel, Eugene Ie, Jason Baldridge HIGHLIGHT: We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset.
357, TITLE: SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning https://www.aclweb.org/anthology/2020.emnlp-main.357 AUTHORS: Tsu-Jui Fu, Xin Wang, Scott Grafton, Miguel Eckstein, William Yang Wang HIGHLIGHT: In this paper, we introduce a Self-Supervised Counterfactual Reasoning (SSCR) framework that incorporates counterfactual thinking to overcome data scarcity.
358, TITLE: Identifying Elements Essential for BERT's Multilinguality https://www.aclweb.org/anthology/2020.emnlp-main.358 AUTHORS: Philipp Dufter, Hinrich Schütze HIGHLIGHT: We aim to identify architectural properties of BERT and linguistic properties of languages that are necessary for BERT to become multilingual.
359, TITLE: On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment https://www.aclweb.org/anthology/2020.emnlp-main.359 AUTHORS: Zirui Wang, Zachary C. Lipton, Yulia Tsvetkov HIGHLIGHT: In this paper, we present the first systematic study of negative interference.
360, TITLE: Pre-tokenization of Multi-word Expressions in Cross-lingual Word Embeddings https://www.aclweb.org/anthology/2020.emnlp-main.360 AUTHORS: Naoki Otani, Satoru Ozaki, Xingyuan Zhao, Yucen Li, Micaelah St Johns, Lori Levin HIGHLIGHT: We propose a simple method for word translation of MWEs to and from English in ten languages: we first compile lists of MWEs in each language and then tokenize the MWEs as single tokens before training word embeddings.
361, TITLE: Monolingual Adapters for Zero-Shot Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.361 AUTHORS: Jerin Philip, Alexandre Berard, Matthias Gallé, Laurent Besacier HIGHLIGHT: We propose a novel adapter layer formalism for adapting multilingual models.
362, TITLE: Do Explicit Alignments Robustly Improve Multilingual Encoders? https://www.aclweb.org/anthology/2020.emnlp-main.362 AUTHORS: Shijie Wu, Mark Dredze HIGHLIGHT: In this paper, we propose a new contrastive alignment objective that can better utilize such signal, and examine whether these previous alignment methods can be adapted to noisier sources of aligned data: a randomly sampled 1 million pair subset of the OPUS collection.
363, TITLE: From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers https://www.aclweb.org/anthology/2020.emnlp-main.363 AUTHORS: Anne Lauscher, Vinit Ravishankar, Ivan Vuli?, Goran Glavaš
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HIGHLIGHT: In this work, we analyze the limitations of downstream language transfer with MMTs, showing that, much like cross-lingual word embeddings, they are substantially less effective in resource-lean scenarios and for distant languages.
364, TITLE: Distilling Multiple Domains for Neural Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.364 AUTHORS: Anna Currey, Prashant Mathur, Georgiana Dinu HIGHLIGHT: In this paper, we propose a framework for training a single multi-domain neural machine translation model that is able to translate several domains without increasing inference time or memory usage.
365, TITLE: Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation https://www.aclweb.org/anthology/2020.emnlp-main.365 AUTHORS: Nils Reimers, Iryna Gurevych HIGHLIGHT: We present an easy and efficient method to extend existing sentence embedding models to new languages.
366, TITLE: A Streaming Approach For Efficient Batched Beam Search https://www.aclweb.org/anthology/2020.emnlp-main.366 AUTHORS: Kevin Yang, Violet Yao, John DeNero, Dan Klein HIGHLIGHT: We propose an efficient batching strategy for variable-length decoding on GPU architectures.
367, TITLE: Improving Multilingual Models with Language-Clustered Vocabularies https://www.aclweb.org/anthology/2020.emnlp-main.367 AUTHORS: Hyung Won Chung, Dan Garrette, Kiat Chuan Tan, Jason Riesa HIGHLIGHT: In this work, we introduce a novel procedure for multilingual vocabulary generation that combines the separately trained vocabularies of several automatically derived language clusters, thus balancing the trade-off between cross-lingual subword sharing and language-specific vocabularies.
368, TITLE: Zero-Shot Cross-Lingual Transfer with Meta Learning https://www.aclweb.org/anthology/2020.emnlp-main.368 AUTHORS: Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva, Isabelle Augenstein HIGHLIGHT: We show that this challenging setup can be approached using meta-learning: in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first.
369, TITLE: The Multilingual Amazon Reviews Corpus https://www.aclweb.org/anthology/2020.emnlp-main.369 AUTHORS: Phillip Keung, Yichao Lu, György Szarvas, Noah A. Smith HIGHLIGHT: We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification.
370, TITLE: GLUCOSE: GeneraLized and COntextualized Story Explanations https://www.aclweb.org/anthology/2020.emnlp-main.370 AUTHORS: Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, Jennifer Chu-Carroll HIGHLIGHT: As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context.
371, TITLE: Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT https://www.aclweb.org/anthology/2020.emnlp-main.371 AUTHORS: Rik van Noord, Antonio Toral, Johan Bos HIGHLIGHT: We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing.
372, TITLE: Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition https://www.aclweb.org/anthology/2020.emnlp-main.372 AUTHORS: Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, James Caverlee HIGHLIGHT: Specifically, we propose a new disease knowledge infusion training procedure and evaluate it on a suite of BERT models including BERT, BioBERT, SciBERT, ClinicalBERT, BlueBERT, and ALBERT.
373, TITLE: Unsupervised Commonsense Question Answering with Self-Talk
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https://www.aclweb.org/anthology/2020.emnlp-main.373 AUTHORS: Vered Shwartz, Peter West, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi HIGHLIGHT: We propose an unsupervised framework based on self-talk as a novel alternative to multiple-choice commonsense tasks.
374, TITLE: Reasoning about Goals, Steps, and Temporal Ordering with WikiHow https://www.aclweb.org/anthology/2020.emnlp-main.374 AUTHORS: Li Zhang, Qing Lyu, Chris Callison-Burch HIGHLIGHT: We propose a suite of reasoning tasks on two types of relations between procedural events: goal-step relations ("learn poses" is a step in the larger goal of "doing yoga") and step-step temporal relations ("buy a yoga mat" typically precedes "learn poses").
375, TITLE: Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models https://www.aclweb.org/anthology/2020.emnlp-main.375 AUTHORS: Ethan Wilcox, Peng Qian, Richard Futrell, Ryosuke Kohita, Roger Levy, Miguel Ballesteros HIGHLIGHT: We find that in most cases, the neural models are able to induce the proper syntactic generalizations after minimal exposure, often from just two examples during training, and that the two structurally supervised models generalize more accurately than the LSTM model.
376, TITLE: Investigating representations of verb bias in neural language models https://www.aclweb.org/anthology/2020.emnlp-main.376 AUTHORS: Robert Hawkins, Takateru Yamakoshi, Thomas Griffiths, Adele Goldberg HIGHLIGHT: Here we introduce DAIS, a large benchmark dataset containing 50K human judgments for 5K distinct sentence pairs in the English dative alternation.
377, TITLE: Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gaze https://www.aclweb.org/anthology/2020.emnlp-main.377 AUTHORS: Ece Takmaz, Sandro Pezzelle, Lisa Beinborn, Raquel Fernández HIGHLIGHT: In this paper, we investigate such sequential cross-modal alignment by modelling the image description generation process computationally.
378, TITLE: Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space https://www.aclweb.org/anthology/2020.emnlp-main.378 AUTHORS: Chunyuan Li, Xiang Gao, Yuan Li, Baolin Peng, Xiujun Li, Yizhe Zhang, Jianfeng Gao HIGHLIGHT: In this paper, we propose the first large-scale language VAE model Optimus (Organizing sentences via Pre- Trained Modeling of a Universal Space).
379, TITLE: BioMegatron: Larger Biomedical Domain Language Model https://www.aclweb.org/anthology/2020.emnlp-main.379 AUTHORS: Hoo-Chang Shin, Yang Zhang, Evelina Bakhturina, Raul Puri, Mostofa Patwary, Mohammad Shoeybi, Raghav Mani HIGHLIGHT: We empirically study and evaluate several factors that can affect performance on domain language applications, such as the sub-word vocabulary set, model size, pre-training corpus, and domain transfer.
380, TITLE: Text Segmentation by Cross Segment Attention https://www.aclweb.org/anthology/2020.emnlp-main.380 AUTHORS: Michal Lukasik, Boris Dadachev, Kishore Papineni, Gonçalo Simões HIGHLIGHT: In this work, we propose three transformer-based architectures and provide comprehensive comparisons with previously proposed approaches on three standard datasets.
381, TITLE: RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark https://www.aclweb.org/anthology/2020.emnlp-main.381 AUTHORS: Tatiana Shavrina, Alena Fenogenova, Emelyanov Anton, Denis Shevelev, Ekaterina Artemova, Valentin Malykh, Vladislav Mikhailov, Maria Tikhonova, Andrey Chertok, Andrey Evlampiev HIGHLIGHT: In this paper, we introduce an advanced Russian general language understanding evaluation benchmark - Russian SuperGLUE.
382, TITLE: An Empirical Study of Pre-trained Transformers for Arabic Information Extraction https://www.aclweb.org/anthology/2020.emnlp-main.382 AUTHORS: Wuwei Lan, Yang Chen, Wei Xu, Alan Ritter
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HIGHLIGHT: In this paper, we pre-train a customized bilingual BERT, dubbed GigaBERT, that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning.
383, TITLE: TNT: Text Normalization based Pre-training of Transformers for Content Moderation https://www.aclweb.org/anthology/2020.emnlp-main.383 AUTHORS: Fei Tan, Yifan Hu, Changwei Hu, Keqian Li, Kevin Yen HIGHLIGHT: In this work, we present a new language pre-training model TNT (Text Normalization based pre-training of Transformers) for content moderation.
384, TITLE: Methods for Numeracy-Preserving Word Embeddings https://www.aclweb.org/anthology/2020.emnlp-main.384 AUTHORS: Dhanasekar Sundararaman, Shijing Si, Vivek Subramanian, Guoyin Wang, Devamanyu Hazarika, Lawrence Carin HIGHLIGHT: We propose a new methodology to assign and learn embeddings for numbers.
385, TITLE: An Empirical Investigation of Contextualized Number Prediction https://www.aclweb.org/anthology/2020.emnlp-main.385 AUTHORS: Taylor Berg-Kirkpatrick, Daniel Spokoyny HIGHLIGHT: Specifically, we introduce a suite of output distribution parameterizations that incorporate latent variables to add expressivity and better fit the natural distribution of numeric values in running text, and combine them with both recur-rent and transformer-based encoder architectures.
386, TITLE: Modeling the Music Genre Perception across Language-Bound Cultures https://www.aclweb.org/anthology/2020.emnlp-main.386 AUTHORS: Elena V. Epure, Guillaume Salha, Manuel Moussallam, Romain Hennequin HIGHLIGHT: In this work, we study the feasibility of obtaining relevant cross-lingual, culture-specific music genre annotations based only on language-specific semantic representations, namely distributed concept embeddings and ontologies.
387, TITLE: Joint Estimation and Analysis of Risk Behavior Ratings in Movie Scripts https://www.aclweb.org/anthology/2020.emnlp-main.387 AUTHORS: Victor Martinez, Krishna Somandepalli, Yalda Tehranian-Uhls, Shrikanth Narayanan HIGHLIGHT: To address this limitation, we propose a model that estimates content ratings based on the language use in movie scripts, making our solution available at the earlier stages of creative production.
388, TITLE: Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit https://www.aclweb.org/anthology/2020.emnlp-main.388 AUTHORS: Amrith Krishna, Ashim Gupta, Deepak Garasangi, Pavankumar Satuluri, Pawan Goyal HIGHLIGHT: We propose a graph-based model for joint morphological parsing and dependency parsing in Sanskrit.
389, TITLE: Unsupervised Parsing via Constituency Tests https://www.aclweb.org/anthology/2020.emnlp-main.389 AUTHORS: Steven Cao, Nikita Kitaev, Dan Klein HIGHLIGHT: We propose a method for unsupervised parsing based on the linguistic notion of a constituency test.
390, TITLE: Please Mind the Root: Decoding Arborescences for Dependency Parsing https://www.aclweb.org/anthology/2020.emnlp-main.390 AUTHORS: Ran Zmigrod, Tim Vieira, Ryan Cotterell HIGHLIGHT: We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly de-grades as the size of the training set decreases.
391, TITLE: Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios https://www.aclweb.org/anthology/2020.emnlp-main.391 AUTHORS: Ramy Eskander, Smaranda Muresan, Michael Collins HIGHLIGHT: We describe a fully unsupervised cross-lingual transfer approach for part-of-speech (POS) tagging under a truly low resource scenario.
392, TITLE: Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders https://www.aclweb.org/anthology/2020.emnlp-main.392
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AUTHORS: Andrew Drozdov, Subendhu Rongali, Yi-Pei Chen, Tim O’Gorman, Mohit Iyyer, Andrew McCallum HIGHLIGHT: In this paper, we discover that while DIORA exhaustively encodes all possible binary trees of a sentence with a soft dynamic program, its vector averaging approach is locally greedy and cannot recover from errors when computing the highest scoring parse tree in bottom-up chart parsing.
393, TITLE: Utility is in the Eye of the User: A Critique of NLP Leaderboards https://www.aclweb.org/anthology/2020.emnlp-main.393 AUTHORS: Kawin Ethayarajh, Dan Jurafsky HIGHLIGHT: In this opinion paper, we study the divergence between what is incentivized by leaderboards and what is useful in practice through the lens of microeconomic theory.
394, TITLE: An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training https://www.aclweb.org/anthology/2020.emnlp-main.394 AUTHORS: Kristjan Arumae, Qing Sun, Parminder Bhatia HIGHLIGHT: In this paper we conduct an empirical investigation into known methods to mitigate CF.
395, TITLE: Analyzing Individual Neurons in Pre-trained Language Models https://www.aclweb.org/anthology/2020.emnlp-main.395 AUTHORS: Nadir Durrani, Hassan Sajjad, Fahim Dalvi, Yonatan Belinkov HIGHLIGHT: We found small subsets of neurons to predict linguistic tasks, with lower level tasks (such as morphology) localized in fewer neurons, compared to higher level task of predicting syntax.
396, TITLE: Dissecting Span Identification Tasks with Performance Prediction https://www.aclweb.org/anthology/2020.emnlp-main.396 AUTHORS: Sean Papay, Roman Klinger, Sebastian Padó HIGHLIGHT: Our contributions are: (a) we identify key properties of span ID tasks that can inform performance prediction; (b) we carry out a large-scale experiment on English data, building a model to predict performance for unseen span ID tasks that can support architecture choices; (c), we investigate the parameters of the meta model, yielding new insights on how model and task properties interact to affect span ID performance.
397, TITLE: Assessing Phrasal Representation and Composition in Transformers https://www.aclweb.org/anthology/2020.emnlp-main.397 AUTHORS: Lang Yu, Allyson Ettinger HIGHLIGHT: In this paper, we present systematic analysis of phrasal representations in state-of-the-art pre-trained transformers.
398, TITLE: Analyzing Redundancy in Pretrained Transformer Models https://www.aclweb.org/anthology/2020.emnlp-main.398 AUTHORS: Fahim Dalvi, Hassan Sajjad, Nadir Durrani, Yonatan Belinkov HIGHLIGHT: In this paper, we study the cause of these limitations by defining a notion of Redundancy, which we categorize into two classes: General Redundancy and Task-specific Redundancy.
399, TITLE: Be More with Less: Hypergraph Attention Networks for Inductive Text Classification https://www.aclweb.org/anthology/2020.emnlp-main.399 AUTHORS: Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, Huan Liu HIGHLIGHT: To address those issues, in this paper, we propose a principled model - hypergraph attention networks (HyperGAT), which can obtain more expressive power with less computational consumption for text representation learning.
400, TITLE: Entities as Experts: Sparse Memory Access with Entity Supervision https://www.aclweb.org/anthology/2020.emnlp-main.400 AUTHORS: Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, Eunsol Choi, Tom Kwiatkowski HIGHLIGHT: We introduce a new model-Entities as Experts (EaE)-that can access distinct memories of the entities mentioned in a piece of text.
401, TITLE: H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network https://www.aclweb.org/anthology/2020.emnlp-main.401 AUTHORS: Shen Wang, Xiaokai Wei, Cicero Nogueira dos Santos, Zhiguo Wang, Ramesh Nallapati, Andrew Arnold, Bing Xiang, Philip S. Yu HIGHLIGHT: To fill this gap, in this paper, we propose Hierarchical Hyperbolic Knowledge Graph Attention Network (H2KGAT), a novel knowledge graph embedding framework, which is able to better model and infer hierarchical relation patterns.
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402, TITLE: Does the Objective Matter? Comparing Training Objectives for Pronoun Resolution https://www.aclweb.org/anthology/2020.emnlp-main.402 AUTHORS: Yordan Yordanov, Oana-Maria Camburu, Vid Kocijan, Thomas Lukasiewicz HIGHLIGHT: In this work, we make a fair comparison of the performance and seed-wise stability of four models that represent the four categories of objectives.
403, TITLE: On Losses for Modern Language Models https://www.aclweb.org/anthology/2020.emnlp-main.403 AUTHORS: Stéphane Aroca-Ouellette, Frank Rudzicz HIGHLIGHT: In this paper, we 1) clarify NSP's effect on BERT pre-training, 2) explore fourteen possible auxiliary pre- training tasks, of which seven are novel to modern language models, and 3) investigate different ways to include multiple tasks into pre-training.
404, TITLE: We Can Detect Your Bias: Predicting the Political Ideology of News Articles https://www.aclweb.org/anthology/2020.emnlp-main.404 AUTHORS: Ramy Baly, Giovanni Da San Martino, James Glass, Preslav Nakov HIGHLIGHT: From a modeling perspective, we propose an adversarial media adaptation, as well as a specially adapted triplet loss.
405, TITLE: Semantic Label Smoothing for Sequence to Sequence Problems https://www.aclweb.org/anthology/2020.emnlp-main.405 AUTHORS: Michal Lukasik, Himanshu Jain, Aditya Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix Yu, Sanjiv Kumar HIGHLIGHT: Unlike these works, in this paper, we propose a technique that smooths over \textit{well formed} relevant sequences that not only have sufficient n-gram overlap with the target sequence, but are also \textit{semantically similar}.
406, TITLE: Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors https://www.aclweb.org/anthology/2020.emnlp-main.406 AUTHORS: Sida Gao, Matthew R. Gormley HIGHLIGHT: In this work, we present an approach for efficiently training and decoding hybrids of graphical models and neural networks based on Gibbs sampling.
407, TITLE: Multilevel Text Alignment with Cross-Document Attention https://www.aclweb.org/anthology/2020.emnlp-main.407 AUTHORS: Xuhui Zhou, Nikolaos Pappas, Noah A. Smith HIGHLIGHT: We propose a new learning approach that equips previously established hierarchical attention encoders for representing documents with a cross-document attention component, enabling structural comparisons across different levels (document-to-document and sentence-to-document).
408, TITLE: Conversational Semantic Parsing https://www.aclweb.org/anthology/2020.emnlp-main.408 AUTHORS: Armen Aghajanyan, Jean Maillard, Akshat Shrivastava, Keith Diedrick, Michael Haeger, Haoran Li, Yashar Mehdad, Veselin Stoyanov, Anuj Kumar, Mike Lewis, Sonal Gupta HIGHLIGHT: In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session.
409, TITLE: Probing Task-Oriented Dialogue Representation from Language Models https://www.aclweb.org/anthology/2020.emnlp-main.409 AUTHORS: Chien-Sheng Wu, Caiming Xiong HIGHLIGHT: The goals of this empirical paper are to 1) investigate probing techniques, especially from the unsupervised mutual information aspect, 2) provide guidelines of pre-trained language model selection for the dialogue research community, 3) find insights of pre-training factors for dialogue application that may be the key to success.
410, TITLE: End-to-End Slot Alignment and Recognition for Cross-Lingual NLU https://www.aclweb.org/anthology/2020.emnlp-main.410 AUTHORS: Weijia Xu, Batool Haider, Saab Mansour HIGHLIGHT: In this work, we propose a novel end-to-end model that learns to align and predict target slot labels jointly for cross-lingual transfer. We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus. We release our MultiATIS++ corpus to the community to continue future research on cross-lingual NLU.
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411, TITLE: Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference https://www.aclweb.org/anthology/2020.emnlp-main.411 AUTHORS: Jianguo Zhang, Kazuma Hashimoto, Wenhao Liu, Chien-Sheng Wu, Yao Wan, Philip Yu, Richard Socher, Caiming Xiong HIGHLIGHT: In this paper, we present a simple yet effective approach, discriminative nearest neighbor classification with deep self-attention.
412, TITLE: Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging https://www.aclweb.org/anthology/2020.emnlp-main.412 AUTHORS: Semih Yavuz, Kazuma Hashimoto, Wenhao Liu, Nitish Shirish Keskar, Richard Socher, Caiming Xiong HIGHLIGHT: In this work, we investigate how to better adapt DA taggers to desired target domains with only unlabeled data.
413, TITLE: Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing https://www.aclweb.org/anthology/2020.emnlp-main.413 AUTHORS: Xilun Chen, Asish Ghoshal, Yashar Mehdad, Luke Zettlemoyer, Sonal Gupta HIGHLIGHT: In this paper, we focus on adapting task-oriented semantic parsers to low-resource domains, and propose a novel method that outperforms a supervised neural model at a 10-fold data reduction.
414, TITLE: Sound Natural: Content Rephrasing in Dialog Systems https://www.aclweb.org/anthology/2020.emnlp-main.414 AUTHORS: Arash Einolghozati, Anchit Gupta, Keith Diedrick, Sonal Gupta HIGHLIGHT: In this paper, we study the problem of rephrasing with messaging as a use case and release a dataset of 3000 pairs of original query and rephrased query.
415, TITLE: Zero-Shot Crosslingual Sentence Simplification https://www.aclweb.org/anthology/2020.emnlp-main.415 AUTHORS: Jonathan Mallinson, Rico Sennrich, Mirella Lapata HIGHLIGHT: We propose a zero-shot modeling framework which transfers simplification knowledge from English to another language (for which no parallel simplification corpus exists) while generalizing across languages and tasks.
416, TITLE: Facilitating the Communication of Politeness through Fine-Grained Paraphrasing https://www.aclweb.org/anthology/2020.emnlp-main.416 AUTHORS: Liye Fu, Susan Fussell, Cristian Danescu-Niculescu-Mizil HIGHLIGHT: In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance.
417, TITLE: CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation https://www.aclweb.org/anthology/2020.emnlp-main.417 AUTHORS: Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed Chi HIGHLIGHT: In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that are known to be invariant to task labels.
418, TITLE: Seq2Edits: Sequence Transduction Using Span-level Edit Operations https://www.aclweb.org/anthology/2020.emnlp-main.418 AUTHORS: Felix Stahlberg, Shankar Kumar HIGHLIGHT: We propose Seq2Edits, an open-vocabulary approach to sequence editing for natural language processing (NLP) tasks with a high degree of overlap between input and output texts.
419, TITLE: Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies https://www.aclweb.org/anthology/2020.emnlp-main.419 AUTHORS: Chris Kedzie, Kathleen McKeown HIGHLIGHT: We study the degree to which neural sequence-to-sequence models exhibit fine-grained controllability when performing natural language generation from a meaning representation.
420, TITLE: Blank Language Models https://www.aclweb.org/anthology/2020.emnlp-main.420 AUTHORS: Tianxiao Shen, Victor Quach, Regina Barzilay, Tommi Jaakkola HIGHLIGHT: We propose Blank Language Model (BLM), a model that generates sequences by dynamically creating and filling in blanks.
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421, TITLE: COD3S: Diverse Generation with Discrete Semantic Signatures https://www.aclweb.org/anthology/2020.emnlp-main.421 AUTHORS: Nathaniel Weir, João Sedoc, Benjamin Van Durme HIGHLIGHT: We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to- sequence (seq2seq) models.
422, TITLE: Automatic Extraction of Rules Governing Morphological Agreement https://www.aclweb.org/anthology/2020.emnlp-main.422 AUTHORS: Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa, David R. Mortensen, Zaid Sheikh, Yulia Tsvetkov, Graham Neubig HIGHLIGHT: In this paper, we take steps towards automating this process by devising an automated framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format.
423, TITLE: Tackling the Low-resource Challenge for Canonical Segmentation https://www.aclweb.org/anthology/2020.emnlp-main.423 AUTHORS: Manuel Mager, Özlem Çetino?lu, Katharina Kann HIGHLIGHT: We explore two new models for the task, borrowing from the closely related area of morphological generation: an LSTM pointer-generator and a sequence-to-sequence model with hard monotonic attention trained with imitation learning.
424, TITLE: IGT2P: From Interlinear Glossed Texts to Paradigms https://www.aclweb.org/anthology/2020.emnlp-main.424 AUTHORS: Sarah Moeller, Ling Liu, Changbing Yang, Katharina Kann, Mans Hulden HIGHLIGHT: We introduce a new task that speeds this process and automatically generates new morphological resources for natural language processing systems: IGT-to-paradigms (IGT2P).
425, TITLE: A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support https://www.aclweb.org/anthology/2020.emnlp-main.425 AUTHORS: Ashish Sharma, Adam Miner, David Atkins, Tim Althoff HIGHLIGHT: In this work, we present a computational approach to understanding how empathy is expressed in online mental health platforms.
426, TITLE: Modeling Protagonist Emotions for Emotion-Aware Storytelling https://www.aclweb.org/anthology/2020.emnlp-main.426 AUTHORS: Faeze Brahman, Snigdha Chaturvedi HIGHLIGHT: In this paper, we present the first study on modeling the emotional trajectory of the protagonist in neural storytelling.
427, TITLE: Help! Need Advice on Identifying Advice https://www.aclweb.org/anthology/2020.emnlp-main.427 AUTHORS: Venkata Subrahmanyan Govindarajan, Benjamin Chen, Rebecca Warholic, Katrin Erk, Junyi Jessy Li HIGHLIGHT: We present preliminary models showing that while pre-trained language models are able to capture advice better than rule-based systems, advice identification is challenging, and we identify directions for future research.
428, TITLE: Quantifying Intimacy in Language https://www.aclweb.org/anthology/2020.emnlp-main.428 AUTHORS: Jiaxin Pei, David Jurgens HIGHLIGHT: Here, we introduce a new computational framework for studying expressions of the intimacy in language with an accompanying dataset and deep learning model for accurately predicting the intimacy level of questions (Pearson r = 0.87).
429, TITLE: Writing Strategies for Science Communication: Data and Computational Analysis https://www.aclweb.org/anthology/2020.emnlp-main.429 AUTHORS: Tal August, Lauren Kim, Katharina Reinecke, Noah A. Smith HIGHLIGHT: We compile a set of writing strategies drawn from a wide range of prescriptive sources and develop an annotation scheme allowing humans to recognize them.
430, TITLE: Weakly Supervised Subevent Knowledge Acquisition https://www.aclweb.org/anthology/2020.emnlp-main.430 AUTHORS: Wenlin Yao, Zeyu Dai, Maitreyi Ramaswamy, Bonan Min, Ruihong Huang
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HIGHLIGHT: Acknowledging the scarcity of subevent knowledge, we propose a weakly supervised approach to extract subevent relation tuples from text and build the first large scale subevent knowledge base.
431, TITLE: Biomedical Event Extraction as Sequence Labeling https://www.aclweb.org/anthology/2020.emnlp-main.431 AUTHORS: Alan Ramponi, Rob van der Goot, Rosario Lombardo, Barbara Plank HIGHLIGHT: We introduce Biomedical Event Extraction as Sequence Labeling (BeeSL), a joint end-to-end neural information extraction model.
432, TITLE: Annotating Temporal Dependency Graphs via Crowdsourcing https://www.aclweb.org/anthology/2020.emnlp-main.432 AUTHORS: Jiarui Yao, Haoling Qiu, Bonan Min, Nianwen Xue HIGHLIGHT: We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text.
433, TITLE: Introducing a New Dataset for Event Detection in Cybersecurity Texts https://www.aclweb.org/anthology/2020.emnlp-main.433 AUTHORS: Hieu Man Duc Trong, Duc Trong Le, Amir Pouran Ben Veyseh, Thuat Nguyen, Thien Huu Nguyen HIGHLIGHT: In particular, to facilitate the future research, we introduce a new dataset for this problem, characterizing the manual annotation for 30 important cybersecurity event types and a large dataset size to develop deep learning models.
434, TITLE: CHARM: Inferring Personal Attributes from Conversations https://www.aclweb.org/anthology/2020.emnlp-main.434 AUTHORS: Anna Tigunova, Andrew Yates, Paramita Mirza, Gerhard Weikum HIGHLIGHT: This paper overcomes this limitation by devising CHARM: a zero-shot learning method that creatively leverages keyword extraction and document retrieval in order to predict attribute values that were never seen during training.
435, TITLE: Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks https://www.aclweb.org/anthology/2020.emnlp-main.435 AUTHORS: Viet Dac Lai, Tuan Ngo Nguyen, Thien Huu Nguyen HIGHLIGHT: In this study, we propose a novel gating mechanism to filter noisy information in the hidden vectors of the GCN models for ED based on the information from the trigger candidate.
436, TITLE: Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events https://www.aclweb.org/anthology/2020.emnlp-main.436 AUTHORS: Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, Yaser Al-Onaizan HIGHLIGHT: In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations.
437, TITLE: How Much Knowledge Can You Pack Into the Parameters of a Language Model? https://www.aclweb.org/anthology/2020.emnlp-main.437 AUTHORS: Adam Roberts, Colin Raffel, Noam Shazeer HIGHLIGHT: In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge.
438, TITLE: EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.438 AUTHORS: Momchil Hardalov, Todor Mihaylov, Dimitrina Zlatkova, Yoan Dinkov, Ivan Koychev, Preslav Nakov HIGHLIGHT: We propose EXAMS - a new benchmark dataset for cross-lingual and multilingual question answering for high school examinations.
439, TITLE: End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems https://www.aclweb.org/anthology/2020.emnlp-main.439 AUTHORS: Siamak Shakeri, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang HIGHLIGHT: We propose an end-to-end approach for synthetic QA data generation.
440, TITLE: Multi-Stage Pre-training for Low-Resource Domain Adaptation
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https://www.aclweb.org/anthology/2020.emnlp-main.440 AUTHORS: Rong Zhang, Revanth Gangi Reddy, Md Arafat Sultan, Vittorio Castelli, Anthony Ferritto, Radu Florian, Efsun Sarioglu Kayi, Salim Roukos, Avi Sil, Todd Ward HIGHLIGHT: We show that extending the vocabulary of the LM with domain-specific terms leads to further gains.
441, TITLE: ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention https://www.aclweb.org/anthology/2020.emnlp-main.441 AUTHORS: Jose Manuel Gomez-Perez, Raúl Ortega HIGHLIGHT: For the first time, this paper taps on the potential of transformer language models and bottom-up and top-down attention to tackle the language and visual understanding challenges this task entails.
442, TITLE: SubjQA: A Dataset for Subjectivity and Review Comprehension https://www.aclweb.org/anthology/2020.emnlp-main.442 AUTHORS: Johannes Bjerva, Nikita Bhutani, Behzad Golshan, Wang-Chiew Tan, Isabelle Augenstein HIGHLIGHT: We find that subjectivity is an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance than found in previous work on sentiment analysis. We develop a new dataset which allows us to investigate this relationship. We release an English QA dataset (SubjQA) based on customer reviews, containing subjectivity annotations for questions and answer spans across 6 domains.
443, TITLE: Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements https://www.aclweb.org/anthology/2020.emnlp-main.443 AUTHORS: Yang Li, Gang Li, Luheng He, Jingjie Zheng, Hong Li, Zhiwei Guan HIGHLIGHT: We propose widget captioning, a novel task for automatically generating language descriptions for UI elements from multimodal input including both the image and the structural representations of user interfaces.
444, TITLE: Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive Learning https://www.aclweb.org/anthology/2020.emnlp-main.444 AUTHORS: Wanyun Cui, Guangyu Zheng, Wei Wang HIGHLIGHT: In this paper, we propose Multimodal Aligned Contrastive Decoupled learning (MACD) network.
445, TITLE: Digital Voicing of Silent Speech https://www.aclweb.org/anthology/2020.emnlp-main.445 AUTHORS: David Gaddy, Dan Klein HIGHLIGHT: In this paper, we consider the task of digitally voicing silent speech, where silently mouthed words are converted to audible speech based on electromyography (EMG) sensor measurements that capture muscle impulses.
446, TITLE: Imitation Attacks and Defenses for Black-box Machine Translation Systems https://www.aclweb.org/anthology/2020.emnlp-main.446 AUTHORS: Eric Wallace, Mitchell Stern, Dawn Song HIGHLIGHT: To mitigate these vulnerabilities, we propose a defense that modifies translation outputs in order to misdirect the optimization of imitation models.
447, TITLE: Sequence-Level Mixed Sample Data Augmentation https://www.aclweb.org/anthology/2020.emnlp-main.447 AUTHORS: Demi Guo, Yoon Kim, Alexander Rush HIGHLIGHT: This work proposes a simple data augmentation approach to encourage compositional behavior in neural models for sequence-to-sequence problems.
448, TITLE: Consistency of a Recurrent Language Model With Respect to Incomplete Decoding https://www.aclweb.org/anthology/2020.emnlp-main.448 AUTHORS: Sean Welleck, Ilia Kulikov, Jaedeok Kim, Richard Yuanzhe Pang, Kyunghyun Cho HIGHLIGHT: Based on these insights, we propose two remedies which address inconsistency: consistent variants of top-k and nucleus sampling, and a self-terminating recurrent language model.
449, TITLE: An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks https://www.aclweb.org/anthology/2020.emnlp-main.449 AUTHORS: Lifu Tu, Tianyu Liu, Kevin Gimpel HIGHLIGHT: In this work, we propose several high-order energy terms to capture complex dependencies among labels in sequence labeling, including several that consider the entire label sequence.
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450, TITLE: Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast---Choose Three https://www.aclweb.org/anthology/2020.emnlp-main.450 AUTHORS: Steven Reich, David Mueller, Nicholas Andrews HIGHLIGHT: In this paper, we study \textit{ensemble distillation} as a general framework for producing well-calibrated structured prediction models while avoiding the prohibitive inference-time cost of ensembles.
451, TITLE: Inducing Target-Specific Latent Structures for Aspect Sentiment Classification https://www.aclweb.org/anthology/2020.emnlp-main.451 AUTHORS: Chenhua Chen, Zhiyang Teng, Yue Zhang HIGHLIGHT: We propose gating mechanisms to dynamically combine information from word dependency graphs and latent graphs which are learned by self-attention networks.
452, TITLE: Affective Event Classification with Discourse-enhanced Self-training https://www.aclweb.org/anthology/2020.emnlp-main.452 AUTHORS: Yuan Zhuang, Tianyu Jiang, Ellen Riloff HIGHLIGHT: Our research introduces new classification models to assign affective polarity to event phrases.
453, TITLE: Deep Weighted MaxSAT for Aspect-based Opinion Extraction https://www.aclweb.org/anthology/2020.emnlp-main.453 AUTHORS: Meixi Wu, Wenya Wang, Sinno Jialin Pan HIGHLIGHT: We adopt the MaxSAT semantics to model logic inference process and smoothly incorporate a weighted version of MaxSAT that connects deep neural networks and a graphical model in a joint framework.
454, TITLE: Multi-view Story Characterization from Movie Plot Synopses and Reviews https://www.aclweb.org/anthology/2020.emnlp-main.454 AUTHORS: Sudipta Kar, Gustavo Aguilar, Mirella Lapata, Thamar Solorio HIGHLIGHT: This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies.
455, TITLE: Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding https://www.aclweb.org/anthology/2020.emnlp-main.455 AUTHORS: Samson Tan, Shafiq Joty, Lav Varshney, Min-Yen Kan HIGHLIGHT: We propose Base-Inflection Encoding (BITE), a method to tokenize English text by reducing inflected words to their base forms before reinjecting the grammatical information as special symbols.
456, TITLE: Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions https://www.aclweb.org/anthology/2020.emnlp-main.456 AUTHORS: Arya D. McCarthy, Adina Williams, Shijia Liu, David Yarowsky, Ryan Cotterell HIGHLIGHT: To quantify the similarity, we define gender systems extensionally, thereby reducing the problem of comparisons between languages' gender systems to cluster evaluation.
457, TITLE: RethinkCWS: Is Chinese Word Segmentation a Solved Task? https://www.aclweb.org/anthology/2020.emnlp-main.457 AUTHORS: Jinlan Fu, Pengfei Liu, Qi Zhang, Xuanjing Huang HIGHLIGHT: In this paper, we take stock of what we have achieved and rethink what's left in the CWS task.
458, TITLE: Learning to Pronounce Chinese Without a Pronunciation Dictionary https://www.aclweb.org/anthology/2020.emnlp-main.458 AUTHORS: Christopher Chu, Scot Fang, Kevin Knight HIGHLIGHT: We demonstrate a program that learns to pronounce Chinese text in Mandarin, without a pronunciation dictionary.
459, TITLE: Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph https://www.aclweb.org/anthology/2020.emnlp-main.459 AUTHORS: Xin Lv, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Wei Zhang, Yichi Zhang, Hao Kong, Suhui Wu HIGHLIGHT: To solve these problems, we propose a multi-hop reasoning model over sparse KGs, by applying novel dynamic anticipation and completion strategies: (1) The anticipation strategy utilizes the latent prediction of embedding-based models to make our model perform more potential path search over sparse KGs.
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460, TITLE: Knowledge Association with Hyperbolic Knowledge Graph Embeddings https://www.aclweb.org/anthology/2020.emnlp-main.460 AUTHORS: Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, Wei Zhang HIGHLIGHT: We propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a hyperbolic transformation.
461, TITLE: Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction https://www.aclweb.org/anthology/2020.emnlp-main.461 AUTHORS: Rujun Han, Yichao Zhou, Nanyun Peng HIGHLIGHT: To address these issues, we propose a framework that enhances deep neural network with distributional constraints constructed by probabilistic domain knowledge.
462, TITLE: TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion https://www.aclweb.org/anthology/2020.emnlp-main.462 AUTHORS: Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, William L. Hamilton HIGHLIGHT: We propose the Temporal Message Passing (TeMP) framework to address these challenges by combining graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques.
463, TITLE: Understanding the Difficulty of Training Transformers https://www.aclweb.org/anthology/2020.emnlp-main.463 AUTHORS: Liyuan Liu, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Jiawei Han HIGHLIGHT: Our objective here is to understand {\_}{\_}what complicates Transformer training{\_}{\_} from both empirical and theoretical perspectives.
464, TITLE: An Empirical Study of Generation Order for Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.464 AUTHORS: William Chan, Mitchell Stern, Jamie Kiros, Jakob Uszkoreit HIGHLIGHT: In this work, we present an empirical study of generation order for machine translation.
465, TITLE: Inference Strategies for Machine Translation with Conditional Masking https://www.aclweb.org/anthology/2020.emnlp-main.465 AUTHORS: Julia Kreutzer, George Foster, Colin Cherry HIGHLIGHT: We identify a thresholding strategy that has advantages over the standard "mask-predict" algorithm, and provide analyses of its behavior on machine translation tasks.
466, TITLE: AmbigQA: Answering Ambiguous Open-domain Questions https://www.aclweb.org/anthology/2020.emnlp-main.466 AUTHORS: Sewon Min, Julian Michael, Hannaneh Hajishirzi, Luke Zettlemoyer HIGHLIGHT: In this paper, we introduce AmbigQA, a new open-domain question answering task which involves finding every plausible answer, and then rewriting the question for each one to resolve the ambiguity.
467, TITLE: Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space https://www.aclweb.org/anthology/2020.emnlp-main.467 AUTHORS: Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Jiancheng Lv, Nan Duan, Ming Zhou HIGHLIGHT: In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks.
468, TITLE: Training Question Answering Models From Synthetic Data https://www.aclweb.org/anthology/2020.emnlp-main.468 AUTHORS: Raul Puri, Ryan Spring, Mohammad Shoeybi, Mostofa Patwary, Bryan Catanzaro HIGHLIGHT: This work aims to narrow this gap by taking advantage of large language models and explores several factors such as model size, quality of pretrained models, scale of data synthesized, and algorithmic choices.
469, TITLE: Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning https://www.aclweb.org/anthology/2020.emnlp-main.469 AUTHORS: Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Tongtong Wu HIGHLIGHT: This paper proposes a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions.
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470, TITLE: Multilingual Offensive Language Identification with Cross-lingual Embeddings https://www.aclweb.org/anthology/2020.emnlp-main.470 AUTHORS: Tharindu Ranasinghe, Marcos Zampieri HIGHLIGHT: In this paper, we take advantage of English data available by applying cross-lingual contextual word embeddings and transfer learning to make predictions in languages with less resources.
471, TITLE: Solving Historical Dictionary Codes with a Neural Language Model https://www.aclweb.org/anthology/2020.emnlp-main.471 AUTHORS: Christopher Chu, Raphael Valenti, Kevin Knight HIGHLIGHT: We solve difficult word-based substitution codes by constructing a decoding lattice and searching that lattice with a neural language model.
472, TITLE: Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments https://www.aclweb.org/anthology/2020.emnlp-main.472 AUTHORS: Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, Lyle Ungar HIGHLIGHT: Inspired by geolocation research, we propose the novel task of Micro-Dialect Identification (MDI) and introduce MARBERT, a new language model with striking abilities to predict a fine-grained variety (as small as that of a city) given a single, short message.
473, TITLE: Investigating African-American Vernacular English in Transformer-Based Text Generation https://www.aclweb.org/anthology/2020.emnlp-main.473 AUTHORS: Sophie Groenwold, Lily Ou, Aesha Parekh, Samhita Honnavalli, Sharon Levy, Diba Mirza, William Yang Wang HIGHLIGHT: We investigate the performance of GPT-2 on AAVE text by creating a dataset of intent-equivalent parallel AAVE/SAE tweet pairs, thereby isolating syntactic structure and AAVE- or SAE-specific language for each pair.
474, TITLE: Iterative Domain-Repaired Back-Translation https://www.aclweb.org/anthology/2020.emnlp-main.474 AUTHORS: Hao-Ran Wei, Zhirui Zhang, Boxing Chen, Weihua Luo HIGHLIGHT: In this paper, we focus on the domain-specific translation with low resources, where in-domain parallel corpora are scarce or nonexistent.
475, TITLE: Dynamic Data Selection and Weighting for Iterative Back-Translation https://www.aclweb.org/anthology/2020.emnlp-main.475 AUTHORS: Zi-Yi Dou, Antonios Anastasopoulos, Graham Neubig HIGHLIGHT: In this paper, we provide insights into this commonly used approach and generalize it to a dynamic curriculum learning strategy, which is applied to iterative back-translation models.
476, TITLE: Revisiting Modularized Multilingual NMT to Meet Industrial Demands https://www.aclweb.org/anthology/2020.emnlp-main.476 AUTHORS: Sungwon Lyu, Bokyung Son, Kichang Yang, Jaekyoung Bae HIGHLIGHT: In this study, we revisit the multilingual neural machine translation model that only share modules among the same languages (M2) as a practical alternative to 1-1 to satisfy industrial requirements.
477, TITLE: LAReQA: Language-Agnostic Answer Retrieval from a Multilingual Pool https://www.aclweb.org/anthology/2020.emnlp-main.477 AUTHORS: Uma Roy, Noah Constant, Rami Al-Rfou, Aditya Barua, Aaron Phillips, Yinfei Yang HIGHLIGHT: We present LAReQA, a challenging new benchmark for language-agnostic answer retrieval from a multilingual candidate pool.
478, TITLE: OCR Post Correction for Endangered Language Texts https://www.aclweb.org/anthology/2020.emnlp-main.478 AUTHORS: Shruti Rijhwani, Antonios Anastasopoulos, Graham Neubig HIGHLIGHT: In this work, we address the task of extracting text from these resources. We create a benchmark dataset of transcriptions for scanned books in three critically endangered languages and present a systematic analysis of how general-purpose OCR tools are not robust to the data-scarce setting of endangered languages.
479, TITLE: X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models https://www.aclweb.org/anthology/2020.emnlp-main.479 AUTHORS: Zhengbao Jiang, Antonios Anastasopoulos, Jun Araki, Haibo Ding, Graham Neubig
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HIGHLIGHT: To assess factual knowledge retrieval in LMs in different languages, we create a multilingual benchmark of cloze-style probes for typologically diverse languages.
480, TITLE: CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs https://www.aclweb.org/anthology/2020.emnlp-main.480 AUTHORS: Ahmed El-Kishky, Vishrav Chaudhary, Francisco Guzmán, Philipp Koehn HIGHLIGHT: In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5% across different language pairs.
481, TITLE: Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation https://www.aclweb.org/anthology/2020.emnlp-main.481 AUTHORS: Mehrad Moradshahi, Giovanni Campagna, Sina Semnani, Silei Xu, Monica Lam HIGHLIGHT: We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language.
482, TITLE: Interactive Refinement of Cross-Lingual Word Embeddings https://www.aclweb.org/anthology/2020.emnlp-main.482 AUTHORS: Michelle Yuan, Mozhi Zhang, Benjamin Van Durme, Leah Findlater, Jordan Boyd-Graber HIGHLIGHT: We introduce CLIME, an interactive system to quickly refine cross-lingual word embeddings for a given classification problem.
483, TITLE: Exploiting Sentence Order in Document Alignment https://www.aclweb.org/anthology/2020.emnlp-main.483 AUTHORS: Brian Thompson, Philipp Koehn HIGHLIGHT: We present a simple document alignment method that incorporates sentence order information in both candidate generation and candidate re-scoring.
484, TITLE: XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation https://www.aclweb.org/anthology/2020.emnlp-main.484 AUTHORS: Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou HIGHLIGHT: In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks.
485, TITLE: AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network https://www.aclweb.org/anthology/2020.emnlp-main.485 AUTHORS: Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu HIGHLIGHT: In this paper, we propose to employ a parallelizable approximate variational inference algorithm for the CRF model.
486, TITLE: HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction https://www.aclweb.org/anthology/2020.emnlp-main.486 AUTHORS: Yu Wang, Yun Li, Hanghang Tong, Ziye Zhu HIGHLIGHT: To address this issue, we present a novel nested NER model named HIT.
487, TITLE: Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks https://www.aclweb.org/anthology/2020.emnlp-main.487 AUTHORS: Yuanhe Tian, Yan Song, Fei Xia HIGHLIGHT: In this paper, we propose attentive graph convolutional networks to enhance neural CCG supertagging through a novel solution of leveraging contextual information.
488, TITLE: DAGA: Data Augmentation with a Generation Approach forLow-resource Tagging Tasks https://www.aclweb.org/anthology/2020.emnlp-main.488 AUTHORS: Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, Chunyan Miao HIGHLIGHT: In this work, we propose a novel augmentation method to generate high quality synthetic data for low-resource tagging tasks with language models trained on the linearized labeled sentences.
489, TITLE: Interpretable Multi-dataset Evaluation for Named Entity Recognition
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https://www.aclweb.org/anthology/2020.emnlp-main.489 AUTHORS: Jinlan Fu, Pengfei Liu, Graham Neubig HIGHLIGHT: In this paper, we present a general methodology for interpretable evaluation for the named entity recognition (NER) task.
490, TITLE: Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots https://www.aclweb.org/anthology/2020.emnlp-main.490 AUTHORS: Yuanmeng Yan, Keqing He, Hong Xu, Sihong Liu, Fanyu Meng, Min Hu, Weiran Xu HIGHLIGHT: In this paper, we propose a robust adversarial model-agnostic slot filling method that explicitly decouples local semantics inherent in open-vocabulary slot words from the global context.
491, TITLE: Plug and Play Autoencoders for Conditional Text Generation https://www.aclweb.org/anthology/2020.emnlp-main.491 AUTHORS: Florian Mai, Nikolaos Pappas, Ivan Montero, Noah A. Smith, James Henderson HIGHLIGHT: We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping within the autoencoder's embedding space, training embedding-to-embedding (Emb2Emb).
492, TITLE: Structure Aware Negative Sampling in Knowledge Graphs https://www.aclweb.org/anthology/2020.emnlp-main.492 AUTHORS: Kian Ahrabian, Aarash Feizi, Yasmin Salehi, William L. Hamilton, Avishek Joey Bose HIGHLIGHT: In this paper, we propose Structure Aware Negative Sampling (SANS), an inexpensive negative sampling strategy that utilizes the rich graph structure by selecting negative samples from a node's $k$-hop neighborhood.
493, TITLE: Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation https://www.aclweb.org/anthology/2020.emnlp-main.493 AUTHORS: Minki Kang, Moonsu Han, Sung Ju Hwang HIGHLIGHT: We propose a method to automatically generate a domain- and task-adaptive maskings of the given text for self- supervised pre-training, such that we can effectively adapt the language model to a particular target task (e.g. question answering).
494, TITLE: Autoregressive Knowledge Distillation through Imitation Learning https://www.aclweb.org/anthology/2020.emnlp-main.494 AUTHORS: Alexander Lin, Jeremy Wohlwend, Howard Chen, Tao Lei HIGHLIGHT: We develop a compression technique for autoregressive models that is driven by an imitation learning perspective on knowledge distillation.
495, TITLE: T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack https://www.aclweb.org/anthology/2020.emnlp-main.495 AUTHORS: Boxin Wang, Hengzhi Pei, Boyuan Pan, Qian Chen, Shuohang Wang, Bo Li HIGHLIGHT: To handle these challenges, we propose a target-controllable adversarial attack framework T3, which is applicable to a range of NLP tasks.
496, TITLE: Structured Pruning of Large Language Models https://www.aclweb.org/anthology/2020.emnlp-main.496 AUTHORS: Ziheng Wang, Jeremy Wohlwend, Tao Lei HIGHLIGHT: We present a generic, structured pruning approach by parameterizing each weight matrix using its low-rank factorization, and adaptively removing rank-1 components during training.
497, TITLE: Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models https://www.aclweb.org/anthology/2020.emnlp-main.497 AUTHORS: Thuy-Trang Vu, Dinh Phung, Gholamreza Haffari HIGHLIGHT: In this paper, we show that careful masking strategies can bridge the knowledge gap of masked language models (MLMs) about the domains more effectively by allocating self-supervision where it is needed.
498, TITLE: BAE: BERT-based Adversarial Examples for Text Classification https://www.aclweb.org/anthology/2020.emnlp-main.498 AUTHORS: Siddhant Garg, Goutham Ramakrishnan HIGHLIGHT: We present BAE, a black box attack for generating adversarial examples using contextual perturbations from a BERT masked language model.
499, TITLE: Adversarial Self-Supervised Data-Free Distillation for Text Classification
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https://www.aclweb.org/anthology/2020.emnlp-main.499 AUTHORS: Xinyin Ma, Yongliang Shen, Gongfan Fang, Chen Chen, Chenghao Jia, Weiming Lu HIGHLIGHT: To tackle this problem, we propose a novel two-stage data-free distillation method, named Adversarial self- Supervised Data-Free Distillation (AS-DFD), which is designed for compressing large-scale transformer-based models (e.g., BERT).
500, TITLE: BERT-ATTACK: Adversarial Attack Against BERT Using BERT https://www.aclweb.org/anthology/2020.emnlp-main.500 AUTHORS: Linyang Li, Ruotian Ma, Qipeng Guo, Xiangyang Xue, Xipeng Qiu HIGHLIGHT: In this paper, we propose \textbf{BERT-Attack}, a high-quality and effective method to generate adversarial samples using pre-trained masked language models exemplified by BERT.
501, TITLE: The Thieves on Sesame Street are Polyglots - Extracting Multilingual Models from Monolingual APIs https://www.aclweb.org/anthology/2020.emnlp-main.501 AUTHORS: Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher HIGHLIGHT: We discover that this extraction process extends to local copies initialized from a pre-trained, multilingual model while the victim remains monolingual.
502, TITLE: When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models https://www.aclweb.org/anthology/2020.emnlp-main.502 AUTHORS: Changlong Yu, Jialong Han, Peifeng Wang, Yangqiu Song, Hongming Zhang, Wilfred Ng, Shuming Shi HIGHLIGHT: We address hypernymy detection, i.e., whether an is-a relationship exists between words (x ,y), with the help of large textual corpora.
503, TITLE: Interpreting Open-Domain Modifiers: Decomposition of Wikipedia Categories into Disambiguated Property- Value Pairs https://www.aclweb.org/anthology/2020.emnlp-main.503 AUTHORS: Marius Pasca HIGHLIGHT: This paper proposes an open-domain method for automatically annotating modifier constituents (20th-century') within Wikipedia categories (20th-century male writers) with properties (date of birth).
504, TITLE: A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation https://www.aclweb.org/anthology/2020.emnlp-main.504 AUTHORS: Ming Wang, Yinglin Wang HIGHLIGHT: In this paper, we propose a Synset Relation-Enhanced Framework (SREF) that leverages sense relations for both sense embedding enhancement and a try-again mechanism that implements WSD again, after obtaining basic sense embeddings from augmented WordNet glosses.
505, TITLE: Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation https://www.aclweb.org/anthology/2020.emnlp-main.505 AUTHORS: Dayiheng Liu, Yeyun Gong, Yu Yan, Jie Fu, Bo Shao, Daxin Jiang, Jiancheng Lv, Nan Duan HIGHLIGHT: In this paper, we propose generating multiple headlines with keyphrases of user interests, whose main idea is to generate multiple keyphrases of interest to users for the news first, and then generate multiple keyphrase-relevant headlines.
506, TITLE: Factual Error Correction for Abstractive Summarization Models https://www.aclweb.org/anthology/2020.emnlp-main.506 AUTHORS: Meng Cao, Yue Dong, Jiapeng Wu, Jackie Chi Kit Cheung HIGHLIGHT: We propose a post-editing corrector module to address this issue by identifying and correcting factual errors in generated summaries.
507, TITLE: Compressive Summarization with Plausibility and Salience Modeling https://www.aclweb.org/anthology/2020.emnlp-main.507 AUTHORS: Shrey Desai, Jiacheng Xu, Greg Durrett HIGHLIGHT: In this work, we propose to relax these explicit syntactic constraints on candidate spans, and instead leave the decision about what to delete to two data-driven criteria: plausibility and salience.
508, TITLE: Understanding Neural Abstractive Summarization Models via Uncertainty https://www.aclweb.org/anthology/2020.emnlp-main.508 AUTHORS: Jiacheng Xu, Shrey Desai, Greg Durrett HIGHLIGHT: In this work, we analyze summarization decoders in both blackbox and whitebox ways by studying on the entropy, or uncertainty, of the model's token-level predictions.
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509, TITLE: Better Highlighting: Creating Sub-Sentence Summary Highlights https://www.aclweb.org/anthology/2020.emnlp-main.509 AUTHORS: Sangwoo Cho, Kaiqiang Song, Chen Li, Dong Yu, Hassan Foroosh, Fei Liu HIGHLIGHT: In this paper, we aim to generate summary highlights to be overlaid on the original documents to make it easier for readers to sift through a large amount of text.
510, TITLE: Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach https://www.aclweb.org/anthology/2020.emnlp-main.510 AUTHORS: Bowen Tan, Lianhui Qin, Eric Xing, Zhiting Hu HIGHLIGHT: In this work, we study summarizing on \textit{arbitrary} aspects relevant to the document, which significantly expands the application of the task in practice.
511, TITLE: BERT-enhanced Relational Sentence Ordering Network https://www.aclweb.org/anthology/2020.emnlp-main.511 AUTHORS: Baiyun Cui, Yingming Li, Zhongfei Zhang HIGHLIGHT: In this paper, we introduce a novel BERT-enhanced Relational Sentence Ordering Network (referred to as BRSON) by leveraging BERT for capturing better dependency relationship among sentences to enhance the coherence modeling for the entire paragraph.
512, TITLE: Online Conversation Disentanglement with Pointer Networks https://www.aclweb.org/anthology/2020.emnlp-main.512 AUTHORS: Tao Yu, Shafiq Joty HIGHLIGHT: In this work, we propose an end-to-end online framework for conversation disentanglement that avoids time- consuming domain-specific feature engineering.
513, TITLE: VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling https://www.aclweb.org/anthology/2020.emnlp-main.513 AUTHORS: Machel Reid, Edison Marrese-Taylor, Yutaka Matsuo HIGHLIGHT: In this paper, we tackle the task of definition modeling, where the goal is to learn to generate definitions of words and phrases.
514, TITLE: Coarse-to-Fine Pre-training for Named Entity Recognition https://www.aclweb.org/anthology/2020.emnlp-main.514 AUTHORS: Xue Mengge, Bowen Yu, Zhenyu Zhang, Tingwen Liu, Yue Zhang, Bin Wang HIGHLIGHT: To this end, we proposea NER-specific pre-training framework to in-ject coarse-to-fine automatically mined entityknowledge into pre-trained models.
515, TITLE: Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment https://www.aclweb.org/anthology/2020.emnlp-main.515 AUTHORS: Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, Zhiyuan Liu, Tat-Seng Chua HIGHLIGHT: In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently.
516, TITLE: Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning https://www.aclweb.org/anthology/2020.emnlp-main.516 AUTHORS: Yi Yang, Arzoo Katiyar HIGHLIGHT: We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference.
517, TITLE: Learning Structured Representations of Entity Names using ActiveLearning and Weak Supervision https://www.aclweb.org/anthology/2020.emnlp-main.517 AUTHORS: Kun Qian, Poornima Chozhiyath Raman, Yunyao Li, Lucian Popa HIGHLIGHT: In this paper, we present a novel learning framework that combines active learning and weak supervision to solve this problem.
518, TITLE: Entity Enhanced BERT Pre-training for Chinese NER https://www.aclweb.org/anthology/2020.emnlp-main.518 AUTHORS: Chen Jia, Yuefeng Shi, Qinrong Yang, Yue Zhang
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HIGHLIGHT: To integrate the lexicon into pre-trained LMs for Chinese NER, we investigate a semi-supervised entity enhanced BERT pre-training method.
519, TITLE: Scalable Zero-shot Entity Linking with Dense Entity Retrieval https://www.aclweb.org/anthology/2020.emnlp-main.519 AUTHORS: Ledell Wu, Fabio Petroni, Martin Josifoski, Sebastian Riedel, Luke Zettlemoyer HIGHLIGHT: This paper introduces a conceptually simple, scalable, and highly effective BERT-based entity linking model, along with an extensive evaluation of its accuracy-speed trade-off.
520, TITLE: A Dataset for Tracking Entities in Open Domain Procedural Text https://www.aclweb.org/anthology/2020.emnlp-main.520 AUTHORS: Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, Eduard Hovy HIGHLIGHT: We present the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary.
521, TITLE: Design Challenges in Low-resource Cross-lingual Entity Linking https://www.aclweb.org/anthology/2020.emnlp-main.521 AUTHORS: Xingyu Fu, Weijia Shi, Xiaodong Yu, Zian Zhao, Dan Roth HIGHLIGHT: This paper provides a thorough analysis of low-resource XEL techniques, focusing on the key step of identifying candidate English Wikipedia titles that correspond to a given foreign language mention.
522, TITLE: Efficient One-Pass End-to-End Entity Linking for Questions https://www.aclweb.org/anthology/2020.emnlp-main.522 AUTHORS: Belinda Z. Li, Sewon Min, Srinivasan Iyer, Yashar Mehdad, Wen-tau Yih HIGHLIGHT: We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass.
523, TITLE: LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention https://www.aclweb.org/anthology/2020.emnlp-main.523 AUTHORS: Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto HIGHLIGHT: In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.
524, TITLE: Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation https://www.aclweb.org/anthology/2020.emnlp-main.524 AUTHORS: Tuhin Chakrabarty, Smaranda Muresan, Nanyun Peng HIGHLIGHT: In this paper, we tackle the problem of simile generation.
525, TITLE: STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation https://www.aclweb.org/anthology/2020.emnlp-main.525 AUTHORS: Nader Akoury, Shufan Wang, Josh Whiting, Stephen Hood, Nanyun Peng, Mohit Iyyer HIGHLIGHT: To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online collaborative storytelling community.
526, TITLE: Substance over Style: Document-Level Targeted Content Transfer https://www.aclweb.org/anthology/2020.emnlp-main.526 AUTHORS: Allison Hegel, Sudha Rao, Asli Celikyilmaz, Bill Dolan HIGHLIGHT: In this work, we introduce the task of document-level targeted content transfer and address it in the recipe domain, with a recipe as the document and a dietary restriction (such as vegan or dairy-free) as the targeted constraint.
527, TITLE: Template Guided Text Generation for Task-Oriented Dialogue https://www.aclweb.org/anthology/2020.emnlp-main.527 AUTHORS: Mihir Kale, Abhinav Rastogi HIGHLIGHT: In this work, we investigate two methods for Natural Language Generation (NLG) using a single domain- independent model across a large number of APIs.
528, TITLE: MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics https://www.aclweb.org/anthology/2020.emnlp-main.528 AUTHORS: Anthony Chen, Gabriel Stanovsky, Sameer Singh, Matt Gardner
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HIGHLIGHT: To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations.
529, TITLE: Plan ahead: Self-Supervised Text Planning for Paragraph Completion Task https://www.aclweb.org/anthology/2020.emnlp-main.529 AUTHORS: Dongyeop Kang, Eduard Hovy HIGHLIGHT: To address that, we propose a self-supervised text planner SSPlanner that predicts what to say first (content prediction), then guides the pretrained language model (surface realization) using the predicted content.
530, TITLE: Inquisitive Question Generation for High Level Text Comprehension https://www.aclweb.org/anthology/2020.emnlp-main.530 AUTHORS: Wei-Jen Ko, Te-yuan Chen, Yiyan Huang, Greg Durrett, Junyi Jessy Li HIGHLIGHT: We introduce INQUISITIVE, a dataset of {\textasciitilde}19K questions that are elicited while a person is reading through a document.
531, TITLE: Towards Persona-Based Empathetic Conversational Models https://www.aclweb.org/anthology/2020.emnlp-main.531 AUTHORS: Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu, Chunyan Miao HIGHLIGHT: To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding.
532, TITLE: Personal Information Leakage Detection in Conversations https://www.aclweb.org/anthology/2020.emnlp-main.532 AUTHORS: Qiongkai Xu, Lizhen Qu, Zeyu Gao, Gholamreza Haffari HIGHLIGHT: In this work, we propose to protect personal information by warning users of detected suspicious sentences generated by conversational assistants.
533, TITLE: Response Selection for Multi-Party Conversations with Dynamic Topic Tracking https://www.aclweb.org/anthology/2020.emnlp-main.533 AUTHORS: Weishi Wang, Steven C.H. Hoi, Shafiq Joty HIGHLIGHT: In this work, we frame response selection as a dynamic topic tracking task to match the topic between the response and relevant conversation context.
534, TITLE: Regularizing Dialogue Generation by Imitating Implicit Scenarios https://www.aclweb.org/anthology/2020.emnlp-main.534 AUTHORS: Shaoxiong Feng, Xuancheng Ren, Hongshen Chen, Bin Sun, Kan Li, Xu Sun HIGHLIGHT: To enable responses that are more meaningful and context-specific, we propose to improve generative dialogue systems from the scenario perspective, where both dialogue history and future conversation are taken into account to implicitly reconstruct the scenario knowledge.
535, TITLE: MovieChats: Chat like Humans in a Closed Domain https://www.aclweb.org/anthology/2020.emnlp-main.535 AUTHORS: Hui Su, Xiaoyu Shen, Zhou Xiao, Zheng Zhang, Ernie Chang, Cheng Zhang, Cheng Niu, Jie Zhou HIGHLIGHT: In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine- grained annotations in hope of pushing the limit of movie-domain chatbots.
536, TITLE: Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art https://www.aclweb.org/anthology/2020.emnlp-main.536 AUTHORS: Jing Lu, Vincent Ng HIGHLIGHT: We present an empirical analysis of state-of-the-art resolvers with the goal of providing the general NLP audience with a better understanding of the state of the art and coreference researchers with directions for future research.
537, TITLE: Semantic Role Labeling Guided Multi-turn Dialogue ReWriter https://www.aclweb.org/anthology/2020.emnlp-main.537 AUTHORS: Kun Xu, Haochen Tan, Linfeng Song, Han Wu, Haisong Zhang, Linqi Song, Dong Yu HIGHLIGHT: In this paper, we propose to use semantic role labeling (SRL), which highlights the core semantic information of who did what to whom, to provide additional guidance for the rewriter model.
538, TITLE: Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations https://www.aclweb.org/anthology/2020.emnlp-main.538
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AUTHORS: Lingzhi Wang, Jing Li, Xingshan Zeng, Haisong Zhang, Kam-Fai Wong HIGHLIGHT: Here, we capture the contextual consistency of a quotation in terms of latent topics, interactions with the dialogue history, and coherence to the query turn's existing contents.
539, TITLE: Profile Consistency Identification for Open-domain Dialogue Agents https://www.aclweb.org/anthology/2020.emnlp-main.539 AUTHORS: Haoyu Song, Yan Wang, Wei-Nan Zhang, Zhengyu Zhao, Ting Liu, Xiaojiang Liu HIGHLIGHT: To facilitate the study of profile consistency identification, we create a large-scale human-annotated dataset with over 110K single-turn conversations and their key-value attribute profiles.
540, TITLE: An Element-aware Multi-representation Model for Law Article Prediction https://www.aclweb.org/anthology/2020.emnlp-main.540 AUTHORS: Huilin Zhong, Junsheng Zhou, Weiguang Qu, Yunfei Long, Yanhui Gu HIGHLIGHT: In this paper, we propose a Law Article Element-aware Multi-representation Model (LEMM), which can make full use of law article information and can be used for multi-label samples.
541, TITLE: Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs https://www.aclweb.org/anthology/2020.emnlp-main.541 AUTHORS: Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren HIGHLIGHT: This paper proposes Recurrent Event Network (RE-Net), a novel autoregressive architecture for predicting future interactions.
542, TITLE: Multi-resolution Annotations for Emoji Prediction https://www.aclweb.org/anthology/2020.emnlp-main.542 AUTHORS: Weicheng Ma, Ruibo Liu, Lili Wang, Soroush Vosoughi HIGHLIGHT: This paper annotates an emoji prediction dataset with passage-level multi-class/multi-label, and aspect-level multi-class annotations.
543, TITLE: Less is More: Attention Supervision with Counterfactuals for Text Classification https://www.aclweb.org/anthology/2020.emnlp-main.543 AUTHORS: Seungtaek Choi, Haeju Park, Jinyoung Yeo, Seung-won Hwang HIGHLIGHT: We aim to leverage human and machine intelligence together for attention supervision.
544, TITLE: MODE-LSTM: A Parameter-efficient Recurrent Network with Multi-Scale for Sentence Classification https://www.aclweb.org/anthology/2020.emnlp-main.544 AUTHORS: Qianli Ma, Zhenxi Lin, Jiangyue Yan, Zipeng Chen, Liuhong Yu HIGHLIGHT: In this paper, we propose a simple yet effective model called Multi-scale Orthogonal inDependEnt LSTM (MODE-LSTM), which not only has effective parameters and good generalization ability, but also considers multiscale n-gram features.
545, TITLE: HSCNN: A Hybrid-Siamese Convolutional Neural Network for Extremely Imbalanced Multi-label Text Classification https://www.aclweb.org/anthology/2020.emnlp-main.545 AUTHORS: Wenshuo Yang, Jiyi Li, Fumiyo Fukumoto, Yanming Ye HIGHLIGHT: We propose a hybrid solution which adapts general networks for the head categories, and few-shot techniques for the tail categories.
546, TITLE: Multi-Stage Pre-training for Automated Chinese Essay Scoring https://www.aclweb.org/anthology/2020.emnlp-main.546 AUTHORS: Wei Song, Kai Zhang, Ruiji Fu, Lizhen Liu, Ting Liu, Miaomiao Cheng HIGHLIGHT: This paper proposes a pre-training based automated Chinese essay scoring method.
547, TITLE: Multi-hop Inference for Question-driven Summarization https://www.aclweb.org/anthology/2020.emnlp-main.547 AUTHORS: Yang Deng, Wenxuan Zhang, Wai Lam HIGHLIGHT: In this work, we propose a novel question-driven abstractive summarization method, Multi-hop Selective Generator (MSG), to incorporate multi-hop reasoning into question-driven summarization and, meanwhile, provide justifications for the generated summaries.
548, TITLE: Towards Interpretable Reasoning over Paragraph Effects in Situation
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https://www.aclweb.org/anthology/2020.emnlp-main.548 AUTHORS: Mucheng Ren, Xiubo Geng, Tao Qin, Heyan Huang, Daxin Jiang HIGHLIGHT: Inspired by human cognitive processes, in this paper we propose a sequential approach for this task which explicitly models each step of the reasoning process with neural network modules.
549, TITLE: Question Directed Graph Attention Network for Numerical Reasoning over Text https://www.aclweb.org/anthology/2020.emnlp-main.549 AUTHORS: Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu HIGHLIGHT: To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph.
550, TITLE: Dense Passage Retrieval for Open-Domain Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.550 AUTHORS: Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen- tau Yih HIGHLIGHT: In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework.
551, TITLE: Distilling Structured Knowledge for Text-Based Relational Reasoning https://www.aclweb.org/anthology/2020.emnlp-main.551 AUTHORS: Jin Dong, Marc-Antoine Rondeau, William L. Hamilton HIGHLIGHT: In this work, we investigate how the structured knowledge of a GNN can be distilled into various NLP models in order to improve their performance.
552, TITLE: Asking without Telling: Exploring Latent Ontologies in Contextual Representations https://www.aclweb.org/anthology/2020.emnlp-main.552 AUTHORS: Julian Michael, Jan A. Botha, Ian Tenney HIGHLIGHT: To investigate this, we introduce latent subclass learning (LSL): a modification to classifier-based probing that induces a latent categorization (or ontology) of the probe's inputs.
553, TITLE: Pretrained Language Model Embryology: The Birth of ALBERT https://www.aclweb.org/anthology/2020.emnlp-main.553 AUTHORS: Cheng-Han Chiang, Sung-Feng Huang, Hung-yi Lee HIGHLIGHT: We thus investigate the developmental process from a set of randomly initialized parameters to a totipotent language model, which we refer to as the embryology of a pretrained language model.
554, TITLE: Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models https://www.aclweb.org/anthology/2020.emnlp-main.554 AUTHORS: Isabel Papadimitriou, Dan Jurafsky HIGHLIGHT: We propose transfer learning as a method for analyzing the encoding of grammatical structure in neural language models.
555, TITLE: What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encoding https://www.aclweb.org/anthology/2020.emnlp-main.555 AUTHORS: Yu-An Wang, Yun-Nung Chen HIGHLIGHT: This paper focuses on providing a new insight of pre-trained position embeddings by feature-level analysis and empirical experiments on most of iconic NLP tasks.
556, TITLE: ``You are grounded!'': Latent Name Artifacts in Pre-trained Language Models https://www.aclweb.org/anthology/2020.emnlp-main.556 AUTHORS: Vered Shwartz, Rachel Rudinger, Oyvind Tafjord HIGHLIGHT: We focus on artifacts associated with the representation of given names (e.g., Donald), which, depending on the corpus, may be associated with specific entities, as indicated by next token prediction (e.g., Trump).
557, TITLE: Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models https://www.aclweb.org/anthology/2020.emnlp-main.557 AUTHORS: Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, Xiang Ren
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HIGHLIGHT: In this paper, we investigate whether and to what extent we can induce numerical commonsense knowledge from PTLMs as well as the robustness of this process.
558, TITLE: Grounded Adaptation for Zero-shot Executable Semantic Parsing https://www.aclweb.org/anthology/2020.emnlp-main.558 AUTHORS: Victor Zhong, Mike Lewis, Sida I. Wang, Luke Zettlemoyer HIGHLIGHT: We propose Grounded Adaptation for Zeroshot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e.g. new database schemas).
559, TITLE: An Imitation Game for Learning Semantic Parsers from User Interaction https://www.aclweb.org/anthology/2020.emnlp-main.559 AUTHORS: Ziyu Yao, Yiqi Tang, Wen-tau Yih, Huan Sun, Yu Su HIGHLIGHT: In this paper, we suggest an alternative, human-in-the-loop methodology for learning semantic parsers directly from users.
560, TITLE: IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation https://www.aclweb.org/anthology/2020.emnlp-main.560 AUTHORS: Yitao Cai, Xiaojun Wan HIGHLIGHT: In this work, in addition to using encoders to capture historic information of user inputs, we propose a database schema interaction graph encoder to utilize historic information of database schema items.
561, TITLE: ``What Do You Mean by That?'' A Parser-Independent Interactive Approach for Enhancing Text-to-SQL https://www.aclweb.org/anthology/2020.emnlp-main.561 AUTHORS: Yuntao Li, Bei Chen, Qian Liu, Yan Gao, Jian-Guang Lou, Yan Zhang, Dongmei Zhang HIGHLIGHT: In this paper, we include human in the loop and present a novel parser-independent interactive approach (PIIA) that interacts with users using multi-choice questions and can easily work with arbitrary parsers.
562, TITLE: DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset https://www.aclweb.org/anthology/2020.emnlp-main.562 AUTHORS: Lijie Wang, Ao Zhang, Kun Wu, Ke Sun, Zhenghua Li, Hua Wu, Min Zhang, Haifeng Wang HIGHLIGHT: This paper presents DuSQL, a larges-scale and pragmatic Chinese dataset for the cross-domain text-to-SQL task, containing 200 databases, 813 tables, and 23,797 question/SQL pairs.
563, TITLE: Mention Extraction and Linking for SQL Query Generation https://www.aclweb.org/anthology/2020.emnlp-main.563 AUTHORS: Jianqiang Ma, Zeyu Yan, Shuai Pang, Yang Zhang, Jianping Shen HIGHLIGHT: To solve these problems, this paper proposes a novel extraction-linking approach, where a unified extractor recognizes all types of slot mentions appearing in the question sentence before a linker maps the recognized columns to the table schema to generate executable SQL queries.
564, TITLE: Re-examining the Role of Schema Linking in Text-to-SQL https://www.aclweb.org/anthology/2020.emnlp-main.564 AUTHORS: Wenqiang Lei, Weixin Wang, Zhixin Ma, Tian Gan, Wei Lu, Min-Yen Kan, Tat-Seng Chua HIGHLIGHT: By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking.
565, TITLE: A Multi-Task Incremental Learning Framework with Category Name Embedding for Aspect-Category Sentiment Analysis https://www.aclweb.org/anthology/2020.emnlp-main.565 AUTHORS: Zehui Dai, Cheng Peng, Huajie Chen, Yadong Ding HIGHLIGHT: In this paper, to make multi-task learning feasible for incremental learning, we proposed Category Name Embedding network (CNE-net).
566, TITLE: Train No Evil: Selective Masking for Task-Guided Pre-Training https://www.aclweb.org/anthology/2020.emnlp-main.566 AUTHORS: Yuxian Gu, Zhengyan Zhang, Xiaozhi Wang, Zhiyuan Liu, Maosong Sun HIGHLIGHT: In this paper, we propose a three-stage framework by adding a task-guided pre-training stage with selective masking between general pre-training and fine-tuning.
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567, TITLE: SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge https://www.aclweb.org/anthology/2020.emnlp-main.567 AUTHORS: Pei Ke, Haozhe Ji, Siyang Liu, Xiaoyan Zhu, Minlie Huang HIGHLIGHT: To benefit the downstream tasks in sentiment analysis, we propose a novel language representation model called SentiLARE, which introduces word-level linguistic knowledge including part-of-speech tag and sentiment polarity (inferred from SentiWordNet) into pre-trained models.
568, TITLE: Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding https://www.aclweb.org/anthology/2020.emnlp-main.568 AUTHORS: Jiaxin Huang, Yu Meng, Fang Guo, Heng Ji, Jiawei Han HIGHLIGHT: In this paper, we propose a weakly-supervised approach for aspect-based sentiment analysis, which uses only a few keywords describing each aspect/sentiment without using any labeled examples.
569, TITLE: APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning https://www.aclweb.org/anthology/2020.emnlp-main.569 AUTHORS: Liying Cheng, Lidong Bing, Qian Yu, Wei Lu, Luo Si HIGHLIGHT: In this paper, we introduce a new argument pair extraction (APE) task on peer review and rebuttal in order to study the contents, the structure and the connections between them.
570, TITLE: Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification https://www.aclweb.org/anthology/2020.emnlp-main.570 AUTHORS: Yunjie Ji, Hao Liu, Bolei He, Xinyan Xiao, Hua Wu, Yanhua Yu HIGHLIGHT: To this end, we propose a novel Diversified Multiple Instance Learning Network (D-MILN), which is able to achieve aspect-level sentiment classification with only document-level weak supervision.
571, TITLE: Identifying Exaggerated Language https://www.aclweb.org/anthology/2020.emnlp-main.571 AUTHORS: Li Kong, Chuanyi Li, Jidong Ge, Bin Luo, Vincent Ng HIGHLIGHT: We contribute to the study of hyperbole by (1) creating a corpus focusing on sentence-level hyperbole detection, (2) performing a statistical and manual analysis of our corpus, and (3) addressing the automatic hyperbole detection task.
572, TITLE: Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis https://www.aclweb.org/anthology/2020.emnlp-main.572 AUTHORS: Chenggong Gong, Jianfei Yu, Rui Xia HIGHLIGHT: To resolve this limitation, we propose an end-to-end framework to jointly perform feature and instance based adaptation for the ABSA task in this paper.
573, TITLE: Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA https://www.aclweb.org/anthology/2020.emnlp-main.573 AUTHORS: Ieva Stali?nait?, Ignacio Iacobacci HIGHLIGHT: We identify the problematic areas for the finetuned RoBERTa, BERT and DistilBERT models through systematic error analysis - basic arithmetic (counting phrases), compositional semantics (negation and Semantic Role Labeling), and lexical semantics (surprisal and antonymy).
574, TITLE: Attention is Not Only a Weight: Analyzing Transformers with Vector Norms https://www.aclweb.org/anthology/2020.emnlp-main.574 AUTHORS: Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, Kentaro Inui HIGHLIGHT: This paper shows that attention weights alone are only one of the two factors that determine the output of attention and proposes a norm-based analysis that incorporates the second factor, the norm of the transformed input vectors.
575, TITLE: F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.575 AUTHORS: Hendrik Schuff, Heike Adel, Ngoc Thang Vu HIGHLIGHT: As a remedy, we propose a hierarchical model and a new regularization term to strengthen the answer- explanation coupling as well as two evaluation scores to quantify the coupling.
576, TITLE: On the Ability and Limitations of Transformers to Recognize Formal Languages https://www.aclweb.org/anthology/2020.emnlp-main.576 AUTHORS: Satwik Bhattamishra, Kabir Ahuja, Navin Goyal HIGHLIGHT: In this work, we systematically study the ability of Transformers to model such languages as well as the role of its individual components in doing so.
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577, TITLE: An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing https://www.aclweb.org/anthology/2020.emnlp-main.577 AUTHORS: Martin Schmitt, Sahand Sharifzadeh, Volker Tresp, Hinrich Schütze HIGHLIGHT: To this end, we present the first approach to unsupervised text generation from KGs and show simultaneously how it can be used for unsupervised semantic parsing.
578, TITLE: DGST: a Dual-Generator Network for Text Style Transfer https://www.aclweb.org/anthology/2020.emnlp-main.578 AUTHORS: Xiao Li, Guanyi Chen, Chenghua Lin, Ruizhe Li HIGHLIGHT: We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer.
579, TITLE: A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving https://www.aclweb.org/anthology/2020.emnlp-main.579 AUTHORS: Qinzhuo Wu, Qi Zhang, Jinlan Fu, Xuanjing Huang HIGHLIGHT: To incorporate external knowledge and global expression information, we propose a novel knowledge-aware sequence-to-tree (KA-S2T) network in which the entities in the problem sequences and their categories are modeled as an entity graph.
580, TITLE: Generating Fact Checking Briefs https://www.aclweb.org/anthology/2020.emnlp-main.580 AUTHORS: Angela Fan, Aleksandra Piktus, Fabio Petroni, Guillaume Wenzek, Marzieh Saeidi, Andreas Vlachos, Antoine Bordes, Sebastian Riedel HIGHLIGHT: To train its components, we introduce QABriefDataset We show that fact checking with briefs - in particular QABriefs - increases the accuracy of crowdworkers by 10% while slightly decreasing the time taken.
581, TITLE: Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction https://www.aclweb.org/anthology/2020.emnlp-main.581 AUTHORS: Mengyun Chen, Tao Ge, Xingxing Zhang, Furu Wei, Ming Zhou HIGHLIGHT: We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC).
582, TITLE: Coreferential Reasoning Learning for Language Representation https://www.aclweb.org/anthology/2020.emnlp-main.582 AUTHORS: Deming Ye, Yankai Lin, Jiaju Du, Zhenghao Liu, Peng Li, Maosong Sun, Zhiyuan Liu HIGHLIGHT: To address this issue, we present CorefBERT, a novel language representation model that can capture the coreferential relations in context.
583, TITLE: Is Graph Structure Necessary for Multi-hop Question Answering? https://www.aclweb.org/anthology/2020.emnlp-main.583 AUTHORS: Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu HIGHLIGHT: In this paper, we investigate whether the graph structure is necessary for textual multi-hop reasoning.
584, TITLE: XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization https://www.aclweb.org/anthology/2020.emnlp-main.584 AUTHORS: Alessandro Raganato, Tommaso Pasini, Jose Camacho-Collados, Mohammad Taher Pilehvar HIGHLIGHT: We put forward a large multilingual benchmark, XL-WiC, featuring gold standards in 12 new languages from varied language families and with different degrees of resource availability, opening room for evaluation scenarios such as zero-shot cross-lingual transfer.
585, TITLE: Generationary or ``How We Went beyond Word Sense Inventories and Learned to Gloss'' https://www.aclweb.org/anthology/2020.emnlp-main.585 AUTHORS: Michele Bevilacqua, Marco Maru, Roberto Navigli HIGHLIGHT: In this paper we show this needs not be the case, and propose a unified model that is able to produce contextually appropriate definitions.
586, TITLE: Probing Pretrained Language Models for Lexical Semantics https://www.aclweb.org/anthology/2020.emnlp-main.586 AUTHORS: Ivan Vuli?, Edoardo Maria Ponti, Robert Litschko, Goran Glavaš, Anna Korhonen
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HIGHLIGHT: In this work, we present a systematic empirical analysis across six typologically diverse languages and five different lexical tasks, addressing the following questions: 1) How do different lexical knowledge extraction strategies (monolingual versus multilingual source LM, out-of-context versus in-context encoding, inclusion of special tokens, and layer-wise averaging) impact performance?
587, TITLE: Cross-lingual Spoken Language Understanding with Regularized Representation Alignment https://www.aclweb.org/anthology/2020.emnlp-main.587 AUTHORS: Zihan Liu, Genta Indra Winata, Peng Xu, Zhaojiang Lin, Pascale Fung HIGHLIGHT: To cope with this issue, we propose a regularization approach to further align word-level and sentence-level representations across languages without any external resource.
588, TITLE: SLURP: A Spoken Language Understanding Resource Package https://www.aclweb.org/anthology/2020.emnlp-main.588 AUTHORS: Emanuele Bastianelli, Andrea Vanzo, Pawel Swietojanski, Verena Rieser HIGHLIGHT: In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement.
589, TITLE: Neural Conversational QA: Learning to Reason vs Exploiting Patterns https://www.aclweb.org/anthology/2020.emnlp-main.589 AUTHORS: Nikhil Verma, Abhishek Sharma, Dhiraj Madan, Danish Contractor, Harshit Kumar, Sachindra Joshi HIGHLIGHT: In this paper we share our findings about the four types of patterns in the ShARC corpus and how the neural models exploit them.
590, TITLE: Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition https://www.aclweb.org/anthology/2020.emnlp-main.590 AUTHORS: Xiangji Zeng, Yunliang Li, Yuchen Zhai, Yin Zhang HIGHLIGHT: In this paper, we decompose the sentence into two parts: entity and context, and rethink the relationship between them and model performance from a causal perspective.
591, TITLE: Understanding Procedural Text using Interactive Entity Networks https://www.aclweb.org/anthology/2020.emnlp-main.591 AUTHORS: Jizhi Tang, Yansong Feng, Dongyan Zhao HIGHLIGHT: In this paper, we propose a novel Interactive Entity Network (IEN), which is a recurrent network with memory equipped cells for state tracking.
592, TITLE: A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land? https://www.aclweb.org/anthology/2020.emnlp-main.592 AUTHORS: Hongyu Lin, Yaojie Lu, Jialong Tang, Xianpei Han, Le Sun, Zhicheng Wei, Nicholas Jing Yuan HIGHLIGHT: As there is no currently available dataset to investigate this problem, this paper proposes to conduct randomization test on standard benchmarks.
593, TITLE: DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion https://www.aclweb.org/anthology/2020.emnlp-main.593 AUTHORS: Zhen Han, Peng Chen, Yunpu Ma, Volker Tresp HIGHLIGHT: To this end, we propose DyERNIE, a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds, where the composed spaces are estimated from the sectional curvatures of underlying data.
594, TITLE: Embedding Words in Non-Vector Space with Unsupervised Graph Learning https://www.aclweb.org/anthology/2020.emnlp-main.594 AUTHORS: Max Ryabinin, Sergei Popov, Liudmila Prokhorenkova, Elena Voita HIGHLIGHT: We introduce GraphGlove: unsupervised graph word representations which are learned end-to-end.
595, TITLE: Debiasing knowledge graph embeddings https://www.aclweb.org/anthology/2020.emnlp-main.595 AUTHORS: Joseph Fisher, Arpit Mittal, Dave Palfrey, Christos Christodoulopoulos
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HIGHLIGHT: We present a novel approach, in which all embeddings are trained to be neutral to sensitive attributes such as gender by default using an adversarial loss.
596, TITLE: Message Passing for Hyper-Relational Knowledge Graphs https://www.aclweb.org/anthology/2020.emnlp-main.596 AUTHORS: Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, Jens Lehmann HIGHLIGHT: In this work, we propose a message passing based graph encoder - StarE capable of modeling such hyper- relational KGs.
597, TITLE: Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations https://www.aclweb.org/anthology/2020.emnlp-main.597 AUTHORS: Taichi Ishiwatari, Yuki Yasuda, Taro Miyazaki, Jun Goto HIGHLIGHT: In this paper, we propose relational position encodings that provide RGAT with sequential information reflecting the relational graph structure.
598, TITLE: BERT Knows Punta Cana is not just beautiful, it's gorgeous: Ranking Scalar Adjectives with Contextualised Representations https://www.aclweb.org/anthology/2020.emnlp-main.598 AUTHORS: Aina Garí Soler, Marianna Apidianaki HIGHLIGHT: We propose a novel BERT-based approach to intensity detection for scalar adjectives.
599, TITLE: Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training https://www.aclweb.org/anthology/2020.emnlp-main.599 AUTHORS: Hai Ye, Qingyu Tan, Ruidan He, Juntao Li, Hwee Tou Ng, Lidong Bing HIGHLIGHT: We explore unsupervised domain adaptation (UDA) in this paper.
600, TITLE: Textual Data Augmentation for Efficient Active Learning on Tiny Datasets https://www.aclweb.org/anthology/2020.emnlp-main.600 AUTHORS: Husam Quteineh, Spyridon Samothrakis, Richard Sutcliffe HIGHLIGHT: In this paper we propose a novel data augmentation approach where guided outputs of a language generation model, e.g. GPT-2, when labeled, can improve the performance of text classifiers through an active learning process.
601, TITLE: ``I'd rather just go to bed'': Understanding Indirect Answers https://www.aclweb.org/anthology/2020.emnlp-main.601 AUTHORS: Annie Louis, Dan Roth, Filip Radlinski HIGHLIGHT: We revisit a pragmatic inference problem in dialog: Understanding indirect responses to questions.
602, TITLE: PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction https://www.aclweb.org/anthology/2020.emnlp-main.602 AUTHORS: Xinyao Ma, Maarten Sap, Hannah Rashkin, Yejin Choi HIGHLIGHT: To address this challenge, we adopt an unsupervised approach using auxiliary supervision with related tasks such as paraphrasing and self-supervision based on a reconstruction loss, building on pretrained language models.
603, TITLE: MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision https://www.aclweb.org/anthology/2020.emnlp-main.603 AUTHORS: Patrick Huber, Giuseppe Carenini HIGHLIGHT: In this work, we present a novel scalable methodology to automatically generate discourse treebanks using distant supervision from sentiment annotated datasets, creating and publishing MEGA-DT, a new large-scale discourse-annotated corpus.
604, TITLE: Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments https://www.aclweb.org/anthology/2020.emnlp-main.604 AUTHORS: Sungho Jeon, Michael Strube HIGHLIGHT: In this work, we propose a coherence model which takes discourse structural information into account without relying on human annotations.
605, TITLE: Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions https://www.aclweb.org/anthology/2020.emnlp-main.605 AUTHORS: Ritam Dutt, Rishabh Joshi, Carolyn Rose
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HIGHLIGHT: Grounded in the politeness theory of Brown and Levinson (1978), we propose a generalized framework for modeling face acts in persuasion conversations, resulting in a reliable coding manual, an annotated corpus, and computational models.
606, TITLE: HABERTOR: An Efficient and Effective Deep Hatespeech Detector https://www.aclweb.org/anthology/2020.emnlp-main.606 AUTHORS: Thanh Tran, Yifan Hu, Changwei Hu, Kevin Yen, Fei Tan, Kyumin Lee, Se Rim Park HIGHLIGHT: We present our HABERTOR model for detecting hatespeech in large scale user-generated content.
607, TITLE: An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels https://www.aclweb.org/anthology/2020.emnlp-main.607 AUTHORS: Ilias Chalkidis, Manos Fergadiotis, Sotiris Kotitsas, Prodromos Malakasiotis, Nikolaos Aletras, Ion Androutsopoulos HIGHLIGHT: Here, for the first time, we empirically evaluate a battery of LMTC methods from vanilla LWANs to hierarchical classification approaches and transfer learning, on frequent, few, and zero-shot learning on three datasets from different domains.
608, TITLE: Which *BERT? A Survey Organizing Contextualized Encoders https://www.aclweb.org/anthology/2020.emnlp-main.608 AUTHORS: Patrick Xia, Shijie Wu, Benjamin Van Durme HIGHLIGHT: We present a survey on language representation learning with the aim of consolidating a series of shared lessons learned across a variety of recent efforts.
609, TITLE: Fact or Fiction: Verifying Scientific Claims https://www.aclweb.org/anthology/2020.emnlp-main.609 AUTHORS: David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi HIGHLIGHT: We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision.
610, TITLE: Semantic Role Labeling as Syntactic Dependency Parsing https://www.aclweb.org/anthology/2020.emnlp-main.610 AUTHORS: Tianze Shi, Igor Malioutov, Ozan Irsoy HIGHLIGHT: Based on this observation, we present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format.
611, TITLE: PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge https://www.aclweb.org/anthology/2020.emnlp-main.611 AUTHORS: Yun He, Zhuoer Wang, Yin Zhang, Ruihong Huang, James Caverlee HIGHLIGHT: We present a new benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge.
612, TITLE: Causal Inference of Script Knowledge https://www.aclweb.org/anthology/2020.emnlp-main.612 AUTHORS: Noah Weber, Rachel Rudinger, Benjamin Van Durme HIGHLIGHT: We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions.
613, TITLE: Towards Debiasing NLU Models from Unknown Biases https://www.aclweb.org/anthology/2020.emnlp-main.613 AUTHORS: Prasetya Ajie Utama, Nafise Sadat Moosavi, Iryna Gurevych HIGHLIGHT: In this work, we present the first step to bridge this gap by introducing a self-debiasing framework that prevents models from mainly utilizing biases without knowing them in advance.
614, TITLE: On the Role of Supervision in Unsupervised Constituency Parsing https://www.aclweb.org/anthology/2020.emnlp-main.614 AUTHORS: Haoyue Shi, Karen Livescu, Kevin Gimpel HIGHLIGHT: We introduce strong baselines for them, by training an existing supervised parsing model (Kitaev and Klein, 2018) on the same labeled examples they access.
615, TITLE: Language Model Prior for Low-Resource Neural Machine Translation
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https://www.aclweb.org/anthology/2020.emnlp-main.615 AUTHORS: Christos Baziotis, Barry Haddow, Alexandra Birch HIGHLIGHT: In this work, we propose a novel approach to incorporate a LM as prior in a neural translation model (TM).
616, TITLE: Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks https://www.aclweb.org/anthology/2020.emnlp-main.616 AUTHORS: Denis Emelin, Ivan Titov, Rico Sennrich HIGHLIGHT: We introduce a method for the prediction of disambiguation errors based on statistical data properties, demonstrating its effectiveness across several domains and model types.
617, TITLE: MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer https://www.aclweb.org/anthology/2020.emnlp-main.617 AUTHORS: Jonas Pfeiffer, Ivan Vuli?, Iryna Gurevych, Sebastian Ruder HIGHLIGHT: We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations.
618, TITLE: Translation Artifacts in Cross-lingual Transfer Learning https://www.aclweb.org/anthology/2020.emnlp-main.618 AUTHORS: Mikel Artetxe, Gorka Labaka, Eneko Agirre HIGHLIGHT: In this paper, we show that such translation process can introduce subtle artifacts that have a notable impact in existing cross-lingual models.
619, TITLE: A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media https://www.aclweb.org/anthology/2020.emnlp-main.619 AUTHORS: Ramit Sawhney, Harshit Joshi, Saumya Gandhi, Rajiv Ratn Shah HIGHLIGHT: In this work, we focus on identifying suicidal intent in English tweets by augmenting linguistic models with historical context.
620, TITLE: Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media https://www.aclweb.org/anthology/2020.emnlp-main.620 AUTHORS: Shamik Roy, Dan Goldwasser HIGHLIGHT: In this paper, we suggest a minimally supervised approach for identifying nuanced frames in news article coverage of politically divisive topics.
621, TITLE: Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News https://www.aclweb.org/anthology/2020.emnlp-main.621 AUTHORS: Nguyen Vo, Kyumin Lee HIGHLIGHT: To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users.
622, TITLE: Fortifying Toxic Speech Detectors Against Veiled Toxicity https://www.aclweb.org/anthology/2020.emnlp-main.622 AUTHORS: Xiaochuang Han, Yulia Tsvetkov HIGHLIGHT: In this work, we propose a framework aimed at fortifying existing toxic speech detectors without a large labeled corpus of veiled toxicity.
623, TITLE: Explainable Automated Fact-Checking for Public Health Claims https://www.aclweb.org/anthology/2020.emnlp-main.623 AUTHORS: Neema Kotonya, Francesca Toni HIGHLIGHT: We present the first study of explainable fact-checking for claims which require specific expertise.
624, TITLE: Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning https://www.aclweb.org/anthology/2020.emnlp-main.624 AUTHORS: Xiaoxiao Guo, Mo Yu, Yupeng Gao, Chuang Gan, Murray Campbell, Shiyu Chang HIGHLIGHT: We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks.
625, TITLE: DORB: Dynamically Optimizing Multiple Rewards with Bandits https://www.aclweb.org/anthology/2020.emnlp-main.625 AUTHORS: Ramakanth Pasunuru, Han Guo, Mohit Bansal
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HIGHLIGHT: Considering the above aspects, in our work, we automate the optimization of multiple metric rewards simultaneously via a multi-armed bandit approach (DORB), where at each round, the bandit chooses which metric reward to optimize next, based on expected arm gains.
626, TITLE: MedFilter: Improving Extraction of Task-relevant Utterances through Integration of Discourse Structure and Ontological Knowledge https://www.aclweb.org/anthology/2020.emnlp-main.626 AUTHORS: Sopan Khosla, Shikhar Vashishth, Jill Fain Lehman, Carolyn Rose HIGHLIGHT: In this paper, we propose the novel modeling approach MedFilter, which addresses these insights in order to increase performance at identifying and categorizing task-relevant utterances, and in so doing, positively impacts performance at a downstream information extraction task.
627, TITLE: Hierarchical Evidence Set Modeling for Automated Fact Extraction and Verification https://www.aclweb.org/anthology/2020.emnlp-main.627 AUTHORS: Shyam Subramanian, Kyumin Lee HIGHLIGHT: Unlike the prior works, in this paper, we propose Hierarchical Evidence Set Modeling (HESM), a framework to extract evidence sets (each of which may contain multiple evidence sentences), and verify a claim to be supported, refuted or not enough info, by encoding and attending the claim and evidence sets at different levels of hierarchy.
628, TITLE: Program Enhanced Fact Verification with Verbalization and Graph Attention Network https://www.aclweb.org/anthology/2020.emnlp-main.628 AUTHORS: Xiaoyu Yang, Feng Nie, Yufei Feng, Quan Liu, Zhigang Chen, Xiaodan Zhu HIGHLIGHT: In this paper, we present a Program-enhanced Verbalization and Graph Attention Network (ProgVGAT) to integrate programs and execution into textual inference models.
629, TITLE: Constrained Fact Verification for FEVER https://www.aclweb.org/anthology/2020.emnlp-main.629 AUTHORS: Adithya Pratapa, Sai Muralidhar Jayanthi, Kavya Nerella HIGHLIGHT: In this work, we propose a new methodology for fact-verification, specifically FEVER, that enforces a closed- world reliance on extracted evidence.
630, TITLE: Entity Linking in 100 Languages https://www.aclweb.org/anthology/2020.emnlp-main.630 AUTHORS: Jan A. Botha, Zifei Shan, Daniel Gillick HIGHLIGHT: We propose a new formulation for multilingual entity linking, where language-specific mentions resolve to a language-agnostic Knowledge Base.
631, TITLE: PatchBERT: Just-in-Time, Out-of-Vocabulary Patching https://www.aclweb.org/anthology/2020.emnlp-main.631 AUTHORS: Sangwhan Moon, Naoaki Okazaki HIGHLIGHT: In our paper, we study a pre-trained multilingual BERT model and analyze the OOV rate on downstream tasks, how it introduces information loss, and as a side-effect, obstructs the potential of the underlying model.
632, TITLE: On the importance of pre-training data volume for compact language models https://www.aclweb.org/anthology/2020.emnlp-main.632 AUTHORS: Vincent Micheli, Martin d’Hoffschmidt, François Fleuret HIGHLIGHT: In an effort towards sustainable practices, we study the impact of pre-training data volume on compact language models.
633, TITLE: BERT-of-Theseus: Compressing BERT by Progressive Module Replacing https://www.aclweb.org/anthology/2020.emnlp-main.633 AUTHORS: Canwen Xu, Wangchunshu Zhou, Tao Ge, Furu Wei, Ming Zhou HIGHLIGHT: In this paper, we propose a novel model compression approach to effectively compress BERT by progressive module replacing.
634, TITLE: Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting https://www.aclweb.org/anthology/2020.emnlp-main.634 AUTHORS: Sanyuan Chen, Yutai Hou, Yiming Cui, Wanxiang Che, Ting Liu, Xiangzhan Yu HIGHLIGHT: To fine-tune with less forgetting, we propose a recall and learn mechanism, which adopts the idea of multi-task learning and jointly learns pretraining tasks and downstream tasks.
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635, TITLE: Exploring and Predicting Transferability across NLP Tasks https://www.aclweb.org/anthology/2020.emnlp-main.635 AUTHORS: Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, Mohit Iyyer HIGHLIGHT: In this paper, we conduct an extensive study of the transferability between 33 NLP tasks across three broad classes of problems (text classification, question answering, and sequence labeling).
636, TITLE: To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging https://www.aclweb.org/anthology/2020.emnlp-main.636 AUTHORS: Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai, Smaranda Muresan, Jie Ma, Faisal Ladhak, Yaser Al-Onaizan HIGHLIGHT: In this work, we investigate how to effectively use unlabeled data: by exploring the task-specific semi- supervised approach, Cross-View Training (CVT) and comparing it with task-agnostic BERT in multiple settings that include domain and task relevant English data.
637, TITLE: Cold-start Active Learning through Self-supervised Language Modeling https://www.aclweb.org/anthology/2020.emnlp-main.637 AUTHORS: Michelle Yuan, Hsuan-Tien Lin, Jordan Boyd-Graber HIGHLIGHT: Therefore, we treat the language modeling loss as a proxy for classification uncertainty.
638, TITLE: Active Learning for BERT: An Empirical Study https://www.aclweb.org/anthology/2020.emnlp-main.638 AUTHORS: Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, Noam Slonim HIGHLIGHT: Here, we present a large-scale empirical study on active learning techniques for BERT-based classification, addressing a diverse set of AL strategies and datasets.
639, TITLE: Transformer Based Multi-Source Domain Adaptation https://www.aclweb.org/anthology/2020.emnlp-main.639 AUTHORS: Dustin Wright, Isabelle Augenstein HIGHLIGHT: Here, we investigate the problem of unsupervised multi-source domain adaptation, where a model is trained on labelled data from multiple source domains and must make predictions on a domain for which no labelled data has been seen.
640, TITLE: Vector-Vector-Matrix Architecture: A Novel Hardware-Aware Framework for Low-Latency Inference in NLP Applications https://www.aclweb.org/anthology/2020.emnlp-main.640 AUTHORS: Matthew Khoury, Rumen Dangovski, Longwu Ou, Preslav Nakov, Yichen Shen, Li Jing HIGHLIGHT: To address this issue, we propose a novel vector-vector-matrix architecture (VVMA), which greatly reduces the latency at inference time for NMT.
641, TITLE: The importance of fillers for text representations of speech transcripts https://www.aclweb.org/anthology/2020.emnlp-main.641 AUTHORS: Tanvi Dinkar, Pierre Colombo, Matthieu Labeau, Chloé Clavel HIGHLIGHT: We explore the possibility of representing them with deep contextualised embeddings, showing improvements on modelling spoken language and two downstream tasks - predicting a speaker's stance and expressed confidence.
642, TITLE: The role of context in neural pitch accent detection in English https://www.aclweb.org/anthology/2020.emnlp-main.642 AUTHORS: Elizabeth Nielsen, Mark Steedman, Sharon Goldwater HIGHLIGHT: We propose a new model for pitch accent detection, inspired by the work of Stehwien et al. (2018), who presented a CNN-based model for this task.
643, TITLE: VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls https://www.aclweb.org/anthology/2020.emnlp-main.643 AUTHORS: Ramit Sawhney, Piyush Khanna, Arshiya Aggarwal, Taru Jain, Puneet Mathur, Rajiv Ratn Shah HIGHLIGHT: Building on existing work, we introduce a neural model for stock volatility prediction that accounts for stock interdependence via graph convolutions while fusing verbal, vocal, and financial features in a semi-supervised multi-task risk forecasting formulation.
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644, TITLE: Effectively pretraining a speech translation decoder with Machine Translation data https://www.aclweb.org/anthology/2020.emnlp-main.644 AUTHORS: Ashkan Alinejad, Anoop Sarkar HIGHLIGHT: In this paper, we will show that by using an adversarial regularizer, we can bring the encoder representations of the ASR and NMT tasks closer even though they are in different modalities, and how this helps us effectively use a pretrained NMT decoder for speech translation.
645, TITLE: A Preliminary Exploration of GANs for Keyphrase Generation https://www.aclweb.org/anthology/2020.emnlp-main.645 AUTHORS: Avinash Swaminathan, Haimin Zhang, Debanjan Mahata, Rakesh Gosangi, Rajiv Ratn Shah, Amanda Stent HIGHLIGHT: We introduce a new keyphrase generation approach using Generative Adversarial Networks (GANs).
646, TITLE: TESA: A Task in Entity Semantic Aggregation for Abstractive Summarization https://www.aclweb.org/anthology/2020.emnlp-main.646 AUTHORS: Clément Jumel, Annie Louis, Jackie Chi Kit Cheung HIGHLIGHT: In this paper, we present a new dataset and task aimed at the semantic aggregation of entities.
647, TITLE: MLSUM: The Multilingual Summarization Corpus https://www.aclweb.org/anthology/2020.emnlp-main.647 AUTHORS: Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano HIGHLIGHT: We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
648, TITLE: Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles https://www.aclweb.org/anthology/2020.emnlp-main.648 AUTHORS: Yao Lu, Yue Dong, Laurent Charlin HIGHLIGHT: We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles.
649, TITLE: Intrinsic Evaluation of Summarization Datasets https://www.aclweb.org/anthology/2020.emnlp-main.649 AUTHORS: Rishi Bommasani, Claire Cardie HIGHLIGHT: We perform the first large-scale evaluation of summarization datasets by introducing 5 intrinsic metrics and applying them to 10 popular datasets.
650, TITLE: Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness https://www.aclweb.org/anthology/2020.emnlp-main.650 AUTHORS: Stefan Larson, Anthony Zheng, Anish Mahendran, Rishi Tekriwal, Adrian Cheung, Eric Guldan, Kevin Leach, Jonathan K. Kummerfeld HIGHLIGHT: We propose a general approach for guiding workers to write more diverse text by iteratively constraining their writing.
651, TITLE: Conversational Semantic Parsing for Dialog State Tracking https://www.aclweb.org/anthology/2020.emnlp-main.651 AUTHORS: Jianpeng Cheng, Devang Agrawal, Héctor Martínez Alonso, Shruti Bhargava, Joris Driesen, Federico Flego, Dain Kaplan, Dimitri Kartsaklis, Lin Li, Dhivya Piraviperumal, Jason D. Williams, Hong Yu, Diarmuid Ó Séaghdha, Anders Johannsen HIGHLIGHT: We describe an encoder-decoder framework for DST with hierarchical representations, which leads to {\textasciitilde}20% improvement over state-of-the-art DST approaches that operate on a flat meaning space of slot-value pairs.
652, TITLE: doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset https://www.aclweb.org/anthology/2020.emnlp-main.652 AUTHORS: Song Feng, Hui Wan, Chulaka Gunasekara, Siva Patel, Sachindra Joshi, Luis Lastras HIGHLIGHT: We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents.
653, TITLE: Interview: Large-scale Modeling of Media Dialog with Discourse Patterns and Knowledge Grounding https://www.aclweb.org/anthology/2020.emnlp-main.653 AUTHORS: Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, Julian McAuley HIGHLIGHT: In this work, we perform the first large-scale analysis of discourse in media dialog and its impact on generative modeling of dialog turns, with a focus on interrogative patterns and use of external knowledge.
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654, TITLE: INSPIRED: Toward Sociable Recommendation Dialog Systems https://www.aclweb.org/anthology/2020.emnlp-main.654 AUTHORS: Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi, Zhou Yu HIGHLIGHT: Therefore, we present INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations.
655, TITLE: Information Seeking in the Spirit of Learning: A Dataset for Conversational Curiosity https://www.aclweb.org/anthology/2020.emnlp-main.655 AUTHORS: Pedro Rodriguez, Paul Crook, Seungwhan Moon, Zhiguang Wang HIGHLIGHT: We incorporate this knowledge into a multi-task model that reproduces human assistant policies and improves over a bert content model by 13 mean reciprocal rank points.
656, TITLE: Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation https://www.aclweb.org/anthology/2020.emnlp-main.656 AUTHORS: Emily Dinan, Angela Fan, Adina Williams, Jack Urbanek, Douwe Kiela, Jason Weston HIGHLIGHT: We consider three techniques to mitigate gender bias: counterfactual data augmentation, targeted data collection, and bias controlled training.
657, TITLE: Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference https://www.aclweb.org/anthology/2020.emnlp-main.657 AUTHORS: Xiaoan Ding, Tianyu Liu, Baobao Chang, Zhifang Sui, Kevin Gimpel HIGHLIGHT: In this paper, we focus on natural language inference (NLI).
658, TITLE: New Protocols and Negative Results for Textual Entailment Data Collection https://www.aclweb.org/anthology/2020.emnlp-main.658 AUTHORS: Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, Emily Pitler HIGHLIGHT: We propose four alternative protocols, each aimed at improving either the ease with which annotators can produce sound training examples or the quality and diversity of those examples.
659, TITLE: The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions https://www.aclweb.org/anthology/2020.emnlp-main.659 AUTHORS: Xiang Zhou, Yixin Nie, Hao Tan, Mohit Bansal HIGHLIGHT: We find that the performance of state-of-the-art models on Natural Language Inference (NLI) and Reading Comprehension (RC) analysis/stress sets can be highly unstable.
660, TITLE: Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start https://www.aclweb.org/anthology/2020.emnlp-main.660 AUTHORS: Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev, Richard Socher, Caiming Xiong HIGHLIGHT: In this work, we introduce Universal Few-shot textual Entailment (UFO-Entail).
661, TITLE: ConjNLI: Natural Language Inference Over Conjunctive Sentences https://www.aclweb.org/anthology/2020.emnlp-main.661 AUTHORS: Swarnadeep Saha, Yixin Nie, Mohit Bansal HIGHLIGHT: Hence, we introduce ConjNLI, a challenge stress-test for natural language inference over conjunctive sentences, where the premise differs from the hypothesis by conjuncts removed, added, or replaced.
662, TITLE: Data and Representation for Turkish Natural Language Inference https://www.aclweb.org/anthology/2020.emnlp-main.662 AUTHORS: Emrah Budur, R?za Özçelik, Tunga Gungor, Christopher Potts HIGHLIGHT: In this paper, we offer a positive response for natural language inference (NLI) in Turkish.
663, TITLE: Multitask Learning for Cross-Lingual Transfer of Broad-coverage Semantic Dependencies https://www.aclweb.org/anthology/2020.emnlp-main.663 AUTHORS: Maryam Aminian, Mohammad Sadegh Rasooli, Mona Diab HIGHLIGHT: We describe a method for developing broad-coverage semantic dependency parsers for languages for which no semantically annotated resource is available.
664, TITLE: Precise Task Formalization Matters in Winograd Schema Evaluations https://www.aclweb.org/anthology/2020.emnlp-main.664
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AUTHORS: Haokun Liu, William Huang, Dhara Mungra, Samuel R. Bowman HIGHLIGHT: We perform an ablation on two Winograd Schema datasets that interpolates between the formalizations used before and after this surge, and find (i) framing the task as multiple choice improves performance dramatically and (ii)several additional techniques, including the reuse of a pretrained language modeling head, can mitigate the model's extreme sensitivity to hyperparameters.
665, TITLE: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training https://www.aclweb.org/anthology/2020.emnlp-main.665 AUTHORS: Joe Stacey, Pasquale Minervini, Haim Dubossarsky, Sebastian Riedel, Tim Rocktäschel HIGHLIGHT: We show that the bias can be reduced in the sentence representations by using an ensemble of adversaries, encouraging the model to jointly decrease the accuracy of these different adversaries while fitting the data.
666, TITLE: SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery https://www.aclweb.org/anthology/2020.emnlp-main.666 AUTHORS: Jiaming Shen, Wenda Qiu, Jingbo Shang, Michelle Vanni, Xiang Ren, Jiawei Han HIGHLIGHT: In this work, we hypothesize that these two tasks are tightly coupled because two synonymous entities tend to have a similar likelihood of belonging to various semantic classes.
667, TITLE: Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction https://www.aclweb.org/anthology/2020.emnlp-main.667 AUTHORS: Tara Safavi, Danai Koutra, Edgar Meij HIGHLIGHT: In this paper we take initial steps toward this direction by investigating the calibration of KGE models, or the extent to which they output confidence scores that reflect the expected correctness of predicted knowledge graph triples.
668, TITLE: Text Graph Transformer for Document Classification https://www.aclweb.org/anthology/2020.emnlp-main.668 AUTHORS: Haopeng Zhang, Jiawei Zhang HIGHLIGHT: We propose a mini-batch text graph sampling method that significantly reduces computing and memory costs to handle large-sized corpus.
669, TITLE: CoDEx: A Comprehensive Knowledge Graph Completion Benchmark https://www.aclweb.org/anthology/2020.emnlp-main.669 AUTHORS: Tara Safavi, Danai Koutra HIGHLIGHT: We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty.
670, TITLE: META: Metadata-Empowered Weak Supervision for Text Classification https://www.aclweb.org/anthology/2020.emnlp-main.670 AUTHORS: Dheeraj Mekala, Xinyang Zhang, Jingbo Shang HIGHLIGHT: In this paper, we propose a novel framework, META, which goes beyond the existing paradigm and leverages metadata as an additional source of weak supervision.
671, TITLE: Towards More Accurate Uncertainty Estimation In Text Classification https://www.aclweb.org/anthology/2020.emnlp-main.671 AUTHORS: Jianfeng He, Xuchao Zhang, Shuo Lei, Zhiqian Chen, Fanglan Chen, Abdulaziz Alhamadani, Bei Xiao, ChangTien Lu HIGHLIGHT: To achieve this, we aim at generating accurate uncertainty score by improving the confidence of winning scores.
672, TITLE: Chapter Captor: Text Segmentation in Novels https://www.aclweb.org/anthology/2020.emnlp-main.672 AUTHORS: Charuta Pethe, Allen Kim, Steve Skiena HIGHLIGHT: Using this annotated data as ground truth after removing structural cues, we present cut-based and neural methods for chapter segmentation, achieving a F1-score of 0.453 on the challenging task of exact break prediction over book-length documents.
673, TITLE: Authorship Attribution for Neural Text Generation https://www.aclweb.org/anthology/2020.emnlp-main.673 AUTHORS: Adaku Uchendu, Thai Le, Kai Shu, Dongwon Lee HIGHLIGHT: In this work, in the context of this Turing Test, we investigate the so-called authorship attribution problem in three versions: (1) given two texts T1 and T2, are both generated by the same method or not?
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674, TITLE: NwQM: A neural quality assessment framework for Wikipedia https://www.aclweb.org/anthology/2020.emnlp-main.674 AUTHORS: Bhanu Prakash Reddy Guda, Sasi Bhushan Seelaboyina, Soumya Sarkar, Animesh Mukherjee HIGHLIGHT: In this paper we propose Neural wikipedia Quality Monitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation.
675, TITLE: Towards Modeling Revision Requirements in wikiHow Instructions https://www.aclweb.org/anthology/2020.emnlp-main.675 AUTHORS: Irshad Bhat, Talita Anthonio, Michael Roth HIGHLIGHT: In this work, we test whether the need for such edits can be predicted automatically.
676, TITLE: Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations https://www.aclweb.org/anthology/2020.emnlp-main.676 AUTHORS: Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Ratn Shah HIGHLIGHT: We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion.
677, TITLE: Natural Language Processing for Achieving Sustainable Development: the Case of Neural Labelling to Enhance Community Profiling https://www.aclweb.org/anthology/2020.emnlp-main.677 AUTHORS: Costanza Conforti, Stephanie Hirmer, Dai Morgan, Marco Basaldella, Yau Ben Or HIGHLIGHT: In this paper, we show the high potential of NLP to enhance project sustainability.
678, TITLE: To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints https://www.aclweb.org/anthology/2020.emnlp-main.678 AUTHORS: Barun Patra, Chala Fufa, Pamela Bhattacharya, Charles Lee HIGHLIGHT: We showcase a novel model for extracting task-specific date-time entities along with their negation constraints.
679, TITLE: Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models https://www.aclweb.org/anthology/2020.emnlp-main.679 AUTHORS: Changmao Li, Elaine Fisher, Rebecca Thomas, Steve Pittard, Vicki Hertzberg, Jinho D. Choi HIGHLIGHT: This paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable candidates.
680, TITLE: Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses https://www.aclweb.org/anthology/2020.emnlp-main.680 AUTHORS: Simon Flachs, Ophélie Lacroix, Helen Yannakoudakis, Marek Rei, Anders Søgaard HIGHLIGHT: We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency.
681, TITLE: Deconstructing word embedding algorithms https://www.aclweb.org/anthology/2020.emnlp-main.681 AUTHORS: Kian Kenyon-Dean, Edward Newell, Jackie Chi Kit Cheung HIGHLIGHT: In this work, we deconstruct Word2vec, GloVe, and others, into a common form, unveiling some of the common conditions that seem to be required for making performant word embeddings.
682, TITLE: Sequential Modelling of the Evolution of Word Representations for Semantic Change Detection https://www.aclweb.org/anthology/2020.emnlp-main.682 AUTHORS: Adam Tsakalidis, Maria Liakata HIGHLIGHT: In this work, we propose three variants of sequential models for detecting semantically shifted words, effectively accounting for the changes in the word representations over time.
683, TITLE: Sparsity Makes Sense: Word Sense Disambiguation Using Sparse Contextualized Word Representations https://www.aclweb.org/anthology/2020.emnlp-main.683 AUTHORS: Gábor Berend
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HIGHLIGHT: In this paper, we demonstrate that by utilizing sparse word representations, it becomes possible to surpass the results of more complex task-specific models on the task of fine-grained all-words word sense disambiguation.
684, TITLE: Exploring Semantic Capacity of Terms https://www.aclweb.org/anthology/2020.emnlp-main.684 AUTHORS: Jie Huang, Zilong Wang, Kevin Chang, Wen-mei Hwu, JinJun Xiong HIGHLIGHT: We introduce and study semantic capacity of terms.
685, TITLE: Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks https://www.aclweb.org/anthology/2020.emnlp-main.685 AUTHORS: Shubham Toshniwal, Sam Wiseman, Allyson Ettinger, Karen Livescu, Kevin Gimpel HIGHLIGHT: We argue that keeping all entities in memory is unnecessary, and we propose a memory-augmented neural network that tracks only a small bounded number of entities at a time, thus guaranteeing a linear runtime in length of document.
686, TITLE: Revealing the Myth of Higher-Order Inference in Coreference Resolution https://www.aclweb.org/anthology/2020.emnlp-main.686 AUTHORS: Liyan Xu, Jinho D. Choi HIGHLIGHT: To make a comprehensive analysis, we implement an end-to-end coreference system as well as four HOI approaches, attended antecedent, entity equalization, span clustering, and cluster merging, where the latter two are our original methods.
687, TITLE: Pre-training Mention Representations in Coreference Models https://www.aclweb.org/anthology/2020.emnlp-main.687 AUTHORS: Yuval Varkel, Amir Globerson HIGHLIGHT: We propose two self-supervised tasks that are closely related to coreference resolution and thus improve mention representation.
688, TITLE: Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning https://www.aclweb.org/anthology/2020.emnlp-main.688 AUTHORS: Deren Lei, Gangrong Jiang, Xiaotao Gu, Kexuan Sun, Yuning Mao, Xiang Ren HIGHLIGHT: In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents.
689, TITLE: Exploring Contextualized Neural Language Models for Temporal Dependency Parsing https://www.aclweb.org/anthology/2020.emnlp-main.689 AUTHORS: Hayley Ross, Jonathon Cai, Bonan Min HIGHLIGHT: In this paper, we develop several variants of BERT-based temporal dependency parser, and show that BERT significantly improves temporal dependency parsing (Zhang and Xue, 2018a).
690, TITLE: Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction https://www.aclweb.org/anthology/2020.emnlp-main.690 AUTHORS: Patrick Hohenecker, Frank Mtumbuka, Vid Kocijan, Thomas Lukasiewicz HIGHLIGHT: In this work, we systematically compare different neural network architectures and training approaches, and improve the performance of the currently best models on the OIE16 benchmark (Stanovsky and Dagan, 2016) by 0.421 F1 score and 0.420 AUC-PR, respectively, in our experiments (i.e., by more than 200% in both cases).
691, TITLE: SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup https://www.aclweb.org/anthology/2020.emnlp-main.691 AUTHORS: Rongzhi Zhang, Yue Yu, Chao Zhang HIGHLIGHT: We propose a simple but effective data augmentation method to improve label efficiency of active sequence labeling.
692, TITLE: AxCell: Automatic Extraction of Results from Machine Learning Papers https://www.aclweb.org/anthology/2020.emnlp-main.692 AUTHORS: Marcin Kardas, Piotr Czapla, Pontus Stenetorp, Sebastian Ruder, Sebastian Riedel, Ross Taylor, Robert Stojnic HIGHLIGHT: In this paper, we present AxCell, an automatic machine learning pipeline for extracting results from papers.
693, TITLE: Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning https://www.aclweb.org/anthology/2020.emnlp-main.693 AUTHORS: Ye Liu, Sheng Zhang, Rui Song, Suo Feng, Yanghua Xiao
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HIGHLIGHT: In this work, we propose a knowledge-guided reinforcement learning (RL) framework for open attribute value extraction.
694, TITLE: DualTKB: A Dual Learning Bridge between Text and Knowledge Base https://www.aclweb.org/anthology/2020.emnlp-main.694 AUTHORS: Pierre Dognin, Igor Melnyk, Inkit Padhi, Cicero Nogueira dos Santos, Payel Das HIGHLIGHT: In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs).
695, TITLE: Incremental Neural Coreference Resolution in Constant Memory https://www.aclweb.org/anthology/2020.emnlp-main.695 AUTHORS: Patrick Xia, João Sedoc, Benjamin Van Durme HIGHLIGHT: In this work, we successfully convert a high-performing model (Joshi et al., 2020), asymptotically reducing its memory usage to constant space with only a 0.3% relative loss in F1 on OntoNotes 5.0.
696, TITLE: Improving Low Compute Language Modeling with In-Domain Embedding Initialisation https://www.aclweb.org/anthology/2020.emnlp-main.696 AUTHORS: Charles Welch, Rada Mihalcea, Jonathan K. Kummerfeld HIGHLIGHT: We show that for our target setting in English, initialising and freezing input embeddings using in-domain data can improve language model performance by providing a useful representation of rare words, and this pattern holds across several different domains.
697, TITLE: KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation https://www.aclweb.org/anthology/2020.emnlp-main.697 AUTHORS: Wenhu Chen, Yu Su, Xifeng Yan, William Yang Wang HIGHLIGHT: In this paper, we propose to leverage pre-training and transfer learning to address this issue.
698, TITLE: POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training https://www.aclweb.org/anthology/2020.emnlp-main.698 AUTHORS: Yizhe Zhang, Guoyin Wang, Chunyuan Li, Zhe Gan, Chris Brockett, Bill Dolan HIGHLIGHT: To address this challenge, we present POINTER (PrOgressive INsertion-based TransformER), a simple yet novel insertion-based approach for hard-constrained text generation.
699, TITLE: Unsupervised Text Style Transfer with Padded Masked Language Models https://www.aclweb.org/anthology/2020.emnlp-main.699 AUTHORS: Eric Malmi, Aliaksei Severyn, Sascha Rothe HIGHLIGHT: We propose Masker, an unsupervised text-editing method for style transfer.
700, TITLE: PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation https://www.aclweb.org/anthology/2020.emnlp-main.700 AUTHORS: Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si HIGHLIGHT: This work presents PALM with a novel scheme that jointly pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus, specifically designed for generating new text conditioned on context.
701, TITLE: Gradient-guided Unsupervised Lexically Constrained Text Generation https://www.aclweb.org/anthology/2020.emnlp-main.701 AUTHORS: Lei Sha HIGHLIGHT: In this paper, we propose a novel method G2LC to solve the lexically-constrained generation as an unsupervised gradient-guided optimization problem.
702, TITLE: TeaForN: Teacher-Forcing with N-grams https://www.aclweb.org/anthology/2020.emnlp-main.702 AUTHORS: Sebastian Goodman, Nan Ding, Radu Soricut HIGHLIGHT: Our proposed method, Teacher-Forcing with N-grams (TeaForN), addresses both these problems directly, through the use of a stack of N decoders trained to decode along a secondary time axis that allows model-parameter updates based on N prediction steps.
703, TITLE: Experience Grounds Language https://www.aclweb.org/anthology/2020.emnlp-main.703
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AUTHORS: Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian HIGHLIGHT: We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.
704, TITLE: Keep CALM and Explore: Language Models for Action Generation in Text-based Games https://www.aclweb.org/anthology/2020.emnlp-main.704 AUTHORS: Shunyu Yao, Rohan Rao, Matthew Hausknecht, Karthik Narasimhan HIGHLIGHT: In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state.
705, TITLE: CapWAP: Image Captioning with a Purpose https://www.aclweb.org/anthology/2020.emnlp-main.705 AUTHORS: Adam Fisch, Kenton Lee, Ming-Wei Chang, Jonathan Clark, Regina Barzilay HIGHLIGHT: In this paper, we propose a new task, Captioning with A Purpose (CapWAP).
706, TITLE: What is More Likely to Happen Next? Video-and-Language Future Event Prediction https://www.aclweb.org/anthology/2020.emnlp-main.706 AUTHORS: Jie Lei, Licheng Yu, Tamara Berg, Mohit Bansal HIGHLIGHT: In this work, we explore whether AI models are able to learn to make such multimodal commonsense next- event predictions.
707, TITLE: X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers https://www.aclweb.org/anthology/2020.emnlp-main.707 AUTHORS: Jaemin Cho, Jiasen Lu, Dustin Schwenk, Hannaneh Hajishirzi, Aniruddha Kembhavi HIGHLIGHT: We introduce X-LXMERT, an extension to LXMERT with training refinements including: discretizing visual representations, using uniform masking with a large range of masking ratios and aligning the right pre-training datasets to the right objectives which enables it to paint.
708, TITLE: Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations https://www.aclweb.org/anthology/2020.emnlp-main.708 AUTHORS: Wanrong Zhu, Xin Wang, Pradyumna Narayana, Kazoo Sone, Sugato Basu, William Yang Wang HIGHLIGHT: In this work, we set forth to design a set of experiments to understand an important but often ignored problem in visually grounded language generation: given that humans have different utilities and visual attention, how will the sample variance in multi-reference datasets affect the models' performance?
709, TITLE: Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube https://www.aclweb.org/anthology/2020.emnlp-main.709 AUTHORS: Jack Hessel, Zhenhai Zhu, Bo Pang, Radu Soricut HIGHLIGHT: We find that visual-textual grounding is indeed possible across previously unexplored video categories, and that pretraining on a more diverse set results in representations that generalize to both non-instructional and instructional domains.
710, TITLE: Hierarchical Graph Network for Multi-hop Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.710 AUTHORS: Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, Jingjing Liu HIGHLIGHT: In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering.
711, TITLE: A Simple Yet Strong Pipeline for HotpotQA https://www.aclweb.org/anthology/2020.emnlp-main.711 AUTHORS: Dirk Groeneveld, Tushar Khot, Mausam, Ashish Sabharwal HIGHLIGHT: Our pipeline has three steps: 1) use BERT to identify potentially relevant sentences \textit{independently} of each other; 2) feed the set of selected sentences as context into a standard BERT span prediction model to choose an answer; and 3) use the sentence selection model, now with the chosen answer, to produce supporting sentences.
712, TITLE: Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning https://www.aclweb.org/anthology/2020.emnlp-main.712 AUTHORS: Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal HIGHLIGHT: Third, our experiments suggest that there hasn’t been much progress in multi-hop QA in the reading comprehension setting.
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713, TITLE: Unsupervised Question Decomposition for Question Answering https://www.aclweb.org/anthology/2020.emnlp-main.713 AUTHORS: Ethan Perez, Patrick Lewis, Wen-tau Yih, Kyunghyun Cho, Douwe Kiela HIGHLIGHT: Specifically, we propose an algorithm for One-to-N Unsupervised Sequence transduction (ONUS) that learns to map one hard, multi-hop question to many simpler, single-hop sub-questions.
714, TITLE: SRLGRN: Semantic Role Labeling Graph Reasoning Network https://www.aclweb.org/anthology/2020.emnlp-main.714 AUTHORS: Chen Zheng, Parisa Kordjamshidi HIGHLIGHT: We propose a graph reasoning network based on the semantic structure of the sentences to learn cross paragraph reasoning paths and find the supporting facts and the answer jointly.
715, TITLE: CancerEmo: A Dataset for Fine-Grained Emotion Detection https://www.aclweb.org/anthology/2020.emnlp-main.715 AUTHORS: Tiberiu Sosea, Cornelia Caragea HIGHLIGHT: To this end, we introduce CancerEmo, an emotion dataset created from an online health community and annotated with eight fine-grained emotions.
716, TITLE: Exploring the Role of Argument Structure in Online Debate Persuasion https://www.aclweb.org/anthology/2020.emnlp-main.716 AUTHORS: Jialu Li, Esin Durmus, Claire Cardie HIGHLIGHT: In this paper, we aim to further investigate the role of discourse structure of the arguments from online debates in their persuasiveness.
717, TITLE: Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations https://www.aclweb.org/anthology/2020.emnlp-main.717 AUTHORS: Emily Allaway, Kathleen McKeown HIGHLIGHT: In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets.
718, TITLE: Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding https://www.aclweb.org/anthology/2020.emnlp-main.718 AUTHORS: Loitongbam Gyanendro Singh, Anasua Mitra, Sanasam Ranbir Singh HIGHLIGHT: This study proposes a heterogeneous multi-layer network-based representation of tweets to generate multiple representations of a tweet and address the above issues.
719, TITLE: Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning https://www.aclweb.org/anthology/2020.emnlp-main.719 AUTHORS: Amir Pouran Ben Veyseh, Nasim Nouri, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen HIGHLIGHT: In this work, we propose to incorporate the syntactic structures of the sentences into the deep learning models for TOWE, leveraging the syntax-based opinion possibility scores and the syntactic connections between the words.
720, TITLE: EmoTag1200: Understanding the Association between Emojis and Emotions https://www.aclweb.org/anthology/2020.emnlp-main.720 AUTHORS: Abu Awal Md Shoeb, Gerard de Melo HIGHLIGHT: In this paper, we seek to explore the connection between emojis and emotions by means of a new dataset consisting of human-solicited association ratings.
721, TITLE: MIME: MIMicking Emotions for Empathetic Response Generation https://www.aclweb.org/anthology/2020.emnlp-main.721 AUTHORS: Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria HIGHLIGHT: We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content.
722, TITLE: Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning https://www.aclweb.org/anthology/2020.emnlp-main.722 AUTHORS: Tao Shen, Yi Mao, Pengcheng He, Guodong Long, Adam Trischler, Weizhu Chen HIGHLIGHT: In this work, we aim at equipping pre-trained language models with structured knowledge.
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723, TITLE: Named Entity Recognition Only from Word Embeddings https://www.aclweb.org/anthology/2020.emnlp-main.723 AUTHORS: Ying Luo, Hai Zhao, Junlang Zhan HIGHLIGHT: In this work, we propose a fully unsupervised NE recognition model which only needs to take informative clues from pre-trained word embeddings.
724, TITLE: Text Classification Using Label Names Only: A Language Model Self-Training Approach https://www.aclweb.org/anthology/2020.emnlp-main.724 AUTHORS: Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, Jiawei Han HIGHLIGHT: In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents.
725, TITLE: Neural Topic Modeling with Cycle-Consistent Adversarial Training https://www.aclweb.org/anthology/2020.emnlp-main.725 AUTHORS: Xuemeng Hu, Rui Wang, Deyu Zhou, Yuxuan Xiong HIGHLIGHT: To overcome such limitations, we propose Topic Modeling with Cycle-consistent Adversarial Training (ToMCAT) and its supervised version sToMCAT.
726, TITLE: Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation https://www.aclweb.org/anthology/2020.emnlp-main.726 AUTHORS: Ruibo Liu, Guangxuan Xu, Chenyan Jia, Weicheng Ma, Lili Wang, Soroush Vosoughi HIGHLIGHT: In this paper, we present a powerful and easy to deploy text augmentation framework, Data Boost, which augments data through reinforcement learning guided conditional generation.
727, TITLE: A State-independent and Time-evolving Network for Early Rumor Detection in Social Media https://www.aclweb.org/anthology/2020.emnlp-main.727 AUTHORS: Rui Xia, Kaizhou Xuan, Jianfei Yu HIGHLIGHT: In this paper, we study automatic rumor detection for in social media at the event level where an event consists of a sequence of posts organized according to the posting time.
728, TITLE: PyMT5: multi-mode translation of natural language and Python code with transformers https://www.aclweb.org/anthology/2020.emnlp-main.728 AUTHORS: Colin Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan HIGHLIGHT: We present an analysis and modeling effort of a large-scale parallel corpus of 26 million Python methods and 7.7 million method-docstring pairs, demonstrating that for docstring and method generation, PyMT5 outperforms similarly-sized auto- regressive language models (GPT2) which were English pre-trained or randomly initialized.
729, TITLE: PathQG: Neural Question Generation from Facts https://www.aclweb.org/anthology/2020.emnlp-main.729 AUTHORS: Siyuan Wang, Zhongyu Wei, Zhihao Fan, Zengfeng Huang, Weijian Sun, Qi Zhang, Xuanjing Huang HIGHLIGHT: In this paper, we explore to incorporate facts in the text for question generation in a comprehensive way.
730, TITLE: What time is it? Temporal Analysis of Novels https://www.aclweb.org/anthology/2020.emnlp-main.730 AUTHORS: Allen Kim, Charuta Pethe, Steve Skiena HIGHLIGHT: To do so, we construct a data set of hourly time phrases from 52,183 fictional books. We then construct a time- of-day classification model that achieves an average error of 2.27 hours.
731, TITLE: COGS: A Compositional Generalization Challenge Based on Semantic Interpretation https://www.aclweb.org/anthology/2020.emnlp-main.731 AUTHORS: Najoung Kim, Tal Linzen HIGHLIGHT: To facilitate the evaluation of the compositional abilities of language processing architectures, we introduce COGS, a semantic parsing dataset based on a fragment of English.
732, TITLE: An Analysis of Natural Language Inference Benchmarks through the Lens of Negation https://www.aclweb.org/anthology/2020.emnlp-main.732 AUTHORS: Md Mosharaf Hossain, Venelin Kovatchev, Pranoy Dutta, Tiffany Kao, Elizabeth Wei, Eduardo Blanco HIGHLIGHT: In this paper, we present a new benchmark for natural language inference in which negation plays a critical role.
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733, TITLE: On the Sentence Embeddings from Pre-trained Language Models https://www.aclweb.org/anthology/2020.emnlp-main.733 AUTHORS: Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, Lei Li HIGHLIGHT: In this paper, we argue that the semantic information in the BERT embeddings is not fully exploited.
734, TITLE: What Can We Learn from Collective Human Opinions on Natural Language Inference Data? https://www.aclweb.org/anthology/2020.emnlp-main.734 AUTHORS: Yixin Nie, Xiang Zhou, Mohit Bansal HIGHLIGHT: We collect ChaosNLI, a dataset with a total of 464,500 annotations to study Collective HumAn OpinionS in oft- used NLI evaluation sets.
735, TITLE: Improving Text Generation with Student-Forcing Optimal Transport https://www.aclweb.org/anthology/2020.emnlp-main.735 AUTHORS: Jianqiao Li, Chunyuan Li, Guoyin Wang, Hao Fu, Yuhchen Lin, Liqun Chen, Yizhe Zhang, Chenyang Tao, Ruiyi Zhang, Wenlin Wang, Dinghan Shen, Qian Yang, Lawrence Carin HIGHLIGHT: To reduce this gap between training and testing, we propose using optimal transport (OT) to match the sequences generated in these two modes.
736, TITLE: UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation https://www.aclweb.org/anthology/2020.emnlp-main.736 AUTHORS: Jian Guan, Minlie Huang HIGHLIGHT: We propose an approach of constructing negative samples by mimicking the errors commonly observed in existing NLG models, including repeated plots, conflicting logic, and long-range incoherence.
737, TITLE: F^2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax https://www.aclweb.org/anthology/2020.emnlp-main.737 AUTHORS: Byung-Ju Choi, Jimin Hong, David Park, Sang Wan Lee HIGHLIGHT: As a simple yet effective remedy, we propose two novel methods, F{\^{}}2-Softmax and MefMax, for a balanced training even with the skewed frequency distribution.
738, TITLE: Partially-Aligned Data-to-Text Generation with Distant Supervision https://www.aclweb.org/anthology/2020.emnlp-main.738 AUTHORS: Zihao Fu, Bei Shi, Wai Lam, Lidong Bing, Zhiyuan Liu HIGHLIGHT: To tackle this new task, we propose a novel distant supervision generation framework.
739, TITLE: Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions https://www.aclweb.org/anthology/2020.emnlp-main.739 AUTHORS: Bodhisattwa Prasad Majumder, Harsh Jhamtani, Taylor Berg-Kirkpatrick, Julian McAuley HIGHLIGHT: In this paper, we propose to expand available persona sentences using existing commonsense knowledge bases and paraphrasing resources to imbue dialog models with access to an expanded and richer set of persona descriptions.
740, TITLE: A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning https://www.aclweb.org/anthology/2020.emnlp-main.740 AUTHORS: Yichi Zhang, Zhijian Ou, Min Hu, Junlan Feng HIGHLIGHT: In this paper we aim at alleviating the reliance on belief state labels in building end-to-end dialog systems, by leveraging unlabeled dialog data towards semi-supervised learning.
741, TITLE: The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection https://www.aclweb.org/anthology/2020.emnlp-main.741 AUTHORS: Zibo Lin, Deng Cai, Yan Wang, Xiaojiang Liu, Haitao Zheng, Shuming Shi HIGHLIGHT: In this work, we show that grayscale data can be automatically constructed without human effort.
742, TITLE: GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems https://www.aclweb.org/anthology/2020.emnlp-main.742 AUTHORS: Lishan Huang, Zheng Ye, Jinghui Qin, Liang Lin, Xiaodan Liang HIGHLIGHT: Capitalized on the topic-level dialogue graph, we propose a new evaluation metric GRADE, which stands for Graph-enhanced Representations for Automatic Dialogue Evaluation.
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743, TITLE: MedDialog: Large-scale Medical Dialogue Datasets https://www.aclweb.org/anthology/2020.emnlp-main.743 AUTHORS: Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie HIGHLIGHT: To facilitate the research and development of medical dialogue systems, we build large-scale medical dialogue datasets - MedDialog, which contain 1) a Chinese dataset with 3.4 million conversations between patients and doctors, 11.3 million utterances, 660.2 million tokens, covering 172 specialties of diseases, and 2) an English dataset with 0.26 million conversations, 0.51 million utterances, 44.53 million tokens, covering 96 specialties of diseases.
744, TITLE: An information theoretic view on selecting linguistic probes https://www.aclweb.org/anthology/2020.emnlp-main.744 AUTHORS: Zining Zhu, Frank Rudzicz HIGHLIGHT: We show this dichotomy is valid information-theoretically. In addition, we find that the ”good probe” criteria proposed by the two papers, *selectivity* (Hewitt and Liang, 2019) and *information gain* (Pimentel et al., 2020), are equivalent – the errors of their approaches are identical (modulo irrelevant terms).
745, TITLE: With Little Power Comes Great Responsibility https://www.aclweb.org/anthology/2020.emnlp-main.745 AUTHORS: Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, Dan Jurafsky HIGHLIGHT: By meta-analyzing a set of existing NLP papers and datasets, we characterize typical power for a variety of settings and conclude that underpowered experiments are common in the NLP literature.
746, TITLE: Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics https://www.aclweb.org/anthology/2020.emnlp-main.746 AUTHORS: Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, Yejin Choi HIGHLIGHT: We introduce Data Maps-a model-based tool to characterize and diagnose datasets.
747, TITLE: Evaluating and Characterizing Human Rationales https://www.aclweb.org/anthology/2020.emnlp-main.747 AUTHORS: Samuel Carton, Anirudh Rathore, Chenhao Tan HIGHLIGHT: To unpack this finding, we propose improved metrics to account for model-dependent baseline performance.
748, TITLE: On Extractive and Abstractive Neural Document Summarization with Transformer Language Models https://www.aclweb.org/anthology/2020.emnlp-main.748 AUTHORS: Jonathan Pilault, Raymond Li, Sandeep Subramanian, Chris Pal HIGHLIGHT: We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization.
749, TITLE: Multi-Fact Correction in Abstractive Text Summarization https://www.aclweb.org/anthology/2020.emnlp-main.749 AUTHORS: Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung, Jingjing Liu HIGHLIGHT: To address this challenge, we propose Span-Fact, a suite of two factual correction models that leverages knowledge learned from question answering models to make corrections in system-generated summaries via span selection.
750, TITLE: Evaluating the Factual Consistency of Abstractive Text Summarization https://www.aclweb.org/anthology/2020.emnlp-main.750 AUTHORS: Wojciech Kryscinski, Bryan McCann, Caiming Xiong, Richard Socher HIGHLIGHT: We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and generated summaries.
751, TITLE: Re-evaluating Evaluation in Text Summarization https://www.aclweb.org/anthology/2020.emnlp-main.751 AUTHORS: Manik Bhandari, Pranav Narayan Gour, Atabak Ashfaq, Pengfei Liu, Graham Neubig HIGHLIGHT: In this paper, we make an attempt to re-evaluate the evaluation method for text summarization: assessing the reliability of automatic metrics using top-scoring system outputs, both abstractive and extractive, on recently popular datasets for both system-level and summary-level evaluation settings.
752, TITLE: VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles https://www.aclweb.org/anthology/2020.emnlp-main.752 AUTHORS: Mingzhe Li, Xiuying Chen, Shen Gao, Zhangming Chan, Dongyan Zhao, Rui Yan
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HIGHLIGHT: Hence, in this paper, we propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem.
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