EMNLP
Collection
Accepted papers for EMNLP (Conference on Empirical Methods in Natural Language Processing), one dataset per year. • 13 items • Updated
paper_id stringlengths 17 19 | title stringlengths 21 137 | paper_url stringlengths 43 45 | authors listlengths 1 24 | abstract large_stringlengths 340 1.76k | anthology_id stringlengths 17 19 | doi stringlengths 29 31 | award stringclasses 2
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2020.emnlp-main.1 | Detecting Attackable Sentences in Arguments | https://aclanthology.org/2020.emnlp-main.1/ | [
"Yohan Jo",
"Seojin Bang",
"Emaad Manzoor",
"Eduard Hovy",
"Chris Reed"
] | Finding attackable sentences in an argument is the first step toward successful refutation in argumentation. We present a first large-scale analysis of sentence attackability in online arguments. We analyze driving reasons for attacks in argumentation and identify relevant characteristics of sentences. We demonstrate t... | 2020.emnlp-main.1 | 10.18653/v1/2020.emnlp-main.1 | null | 2010.02660 | title_snapshot |
2020.emnlp-main.2 | Extracting Implicitly Asserted Propositions in Argumentation | https://aclanthology.org/2020.emnlp-main.2/ | [
"Yohan Jo",
"Jacky Visser",
"Chris Reed",
"Eduard Hovy"
] | Argumentation accommodates various rhetorical devices, such as questions, reported speech, and imperatives. These rhetorical tools usually assert argumentatively relevant propositions rather implicitly, so understanding their true meaning is key to understanding certain arguments properly. However, most argument mining... | 2020.emnlp-main.2 | 10.18653/v1/2020.emnlp-main.2 | null | 2010.02654 | title_snapshot |
2020.emnlp-main.3 | Quantitative argument summarization and beyond: Cross-domain key point analysis | https://aclanthology.org/2020.emnlp-main.3/ | [
"Roy Bar-Haim",
"Yoav Kantor",
"Lilach Eden",
"Roni Friedman",
"Dan Lahav",
"Noam Slonim"
] | When summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence. Work on multi-document summarization has traditionally focused on creating textual summaries, which lack this quantitative aspect. Recent... | 2020.emnlp-main.3 | 10.18653/v1/2020.emnlp-main.3 | null | 2010.05369 | title_snapshot |
2020.emnlp-main.4 | Unsupervised stance detection for arguments from consequences | https://aclanthology.org/2020.emnlp-main.4/ | [
"Jonathan Kobbe",
"Ioana Hulpuș",
"Heiner Stuckenschmidt"
] | Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions. In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. Most related work focuses on topic-specific supervised models tha... | 2020.emnlp-main.4 | 10.18653/v1/2020.emnlp-main.4 | null | null | null |
2020.emnlp-main.5 | BLEU might be Guilty but References are not Innocent | https://aclanthology.org/2020.emnlp-main.5/ | [
"Markus Freitag",
"David Grangier",
"Isaac Caswell"
] | The quality of automatic metrics for machine translation has been increasingly called into question, especially for high-quality systems. This paper demonstrates that, while choice of metric is important, the nature of the references is also critical. We study different methods to collect references and compare their v... | 2020.emnlp-main.5 | 10.18653/v1/2020.emnlp-main.5 | null | 2004.06063 | title_snapshot |
2020.emnlp-main.6 | Statistical Power and Translationese in Machine Translation Evaluation | https://aclanthology.org/2020.emnlp-main.6/ | [
"Yvette Graham",
"Barry Haddow",
"Philipp Koehn"
] | 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. Our analysis shows differences in conclusions drawn from evaluations that include translationese in test data c... | 2020.emnlp-main.6 | 10.18653/v1/2020.emnlp-main.6 | null | null | null |
2020.emnlp-main.7 | Simulated multiple reference training improves low-resource machine translation | https://aclanthology.org/2020.emnlp-main.7/ | [
"Huda Khayrallah",
"Brian Thompson",
"Matt Post",
"Philipp Koehn"
] | Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible transla... | 2020.emnlp-main.7 | 10.18653/v1/2020.emnlp-main.7 | null | 2004.14524 | title_snapshot |
2020.emnlp-main.8 | Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing | https://aclanthology.org/2020.emnlp-main.8/ | [
"Brian Thompson",
"Matt Post"
] | We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. We propose training the paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot translation task (e.g., Czech to Czech). This... | 2020.emnlp-main.8 | 10.18653/v1/2020.emnlp-main.8 | null | 2004.14564 | title_snapshot |
2020.emnlp-main.9 | PRover: Proof Generation for Interpretable Reasoning over Rules | https://aclanthology.org/2020.emnlp-main.9/ | [
"Swarnadeep Saha",
"Sayan Ghosh",
"Shashank Srivastava",
"Mohit Bansal"
] | Recent work by Clark et al. (2020) shows that transformers can act as “soft theorem provers” by answering questions over explicitly provided knowledge in natural language. In our work, we take a step closer to emulating formal theorem provers, by proposing PRover, an interpretable transformer-based model that jointly a... | 2020.emnlp-main.9 | 10.18653/v1/2020.emnlp-main.9 | null | 2010.02830 | title_snapshot |
2020.emnlp-main.10 | Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering | https://aclanthology.org/2020.emnlp-main.10/ | [
"Harsh Jhamtani",
"Peter Clark"
] | Despite the rapid progress in multihop question-answering (QA), models still have trouble explaining why an answer is correct, with limited explanation training data available to learn from. To address this, we introduce three explanation datasets in which explanations formed from corpus facts are annotated. Our first ... | 2020.emnlp-main.10 | 10.18653/v1/2020.emnlp-main.10 | null | 2010.03274 | title_snapshot |
2020.emnlp-main.11 | Self-Supervised Knowledge Triplet Learning for Zero-Shot Question Answering | https://aclanthology.org/2020.emnlp-main.11/ | [
"Pratyay Banerjee",
"Chitta Baral"
] | The aim of all Question Answering (QA) systems is to generalize to unseen questions. Current supervised methods are reliant on expensive data annotation. Moreover, such annotations can introduce unintended annotator bias, making systems focus more on the bias than the actual task. This work proposes Knowledge Triplet L... | 2020.emnlp-main.11 | 10.18653/v1/2020.emnlp-main.11 | null | 2005.00316 | title_snapshot |
2020.emnlp-main.12 | More Bang for Your Buck: Natural Perturbation for Robust Question Answering | https://aclanthology.org/2020.emnlp-main.12/ | [
"Daniel Khashabi",
"Tushar Khot",
"Ashish Sabharwal"
] | Deep learning models for linguistic tasks require large training datasets, which are expensive to create. 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-driv... | 2020.emnlp-main.12 | 10.18653/v1/2020.emnlp-main.12 | null | 2004.04849 | title_snapshot |
2020.emnlp-main.13 | A matter of framing: The impact of linguistic formalism on probing results | https://aclanthology.org/2020.emnlp-main.13/ | [
"Ilia Kuznetsov",
"Iryna Gurevych"
] | Deep pre-trained contextualized encoders like BERT demonstrate remarkable performance on a range of downstream tasks. A recent line of research in probing investigates the linguistic knowledge implicitly learned by these models during pre-training. While most work in probing operates on the task level, linguistic tasks... | 2020.emnlp-main.13 | 10.18653/v1/2020.emnlp-main.13 | null | 2004.14999 | title_snapshot |
2020.emnlp-main.14 | Information-Theoretic Probing with Minimum Description Length | https://aclanthology.org/2020.emnlp-main.14/ | [
"Elena Voita",
"Ivan Titov"
] | To measure how well pretrained representations encode some linguistic property, it is common to use accuracy of a probe, i.e. a classifier trained to predict the property from the representations. Despite widespread adoption of probes, differences in their accuracy fail to adequately reflect differences in representati... | 2020.emnlp-main.14 | 10.18653/v1/2020.emnlp-main.14 | null | 2003.12298 | title_snapshot |
2020.emnlp-main.15 | Intrinsic Probing through Dimension Selection | https://aclanthology.org/2020.emnlp-main.15/ | [
"Lucas Torroba Hennigen",
"Adina Williams",
"Ryan Cotterell"
] | Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks. Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it. I... | 2020.emnlp-main.15 | 10.18653/v1/2020.emnlp-main.15 | null | 2010.02812 | title_snapshot |
2020.emnlp-main.16 | Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually) | https://aclanthology.org/2020.emnlp-main.16/ | [
"Alex Warstadt",
"Yian Zhang",
"Xiaocheng Li",
"Haokun Liu",
"Samuel R. Bowman"
] | One reason pretraining on self-supervised linguistic tasks is effective is that it teaches models features that are helpful for language understanding. However, we want pretrained models to learn not only to represent linguistic features, but also to use those features preferentially during fine-turning. With this goal... | 2020.emnlp-main.16 | 10.18653/v1/2020.emnlp-main.16 | null | 2010.05358 | title_snapshot |
2020.emnlp-main.17 | Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference | https://aclanthology.org/2020.emnlp-main.17/ | [
"Bang An",
"Jie Lyu",
"Zhenyi Wang",
"Chunyuan Li",
"Changwei Hu",
"Fei Tan",
"Ruiyi Zhang",
"Yifan Hu",
"Changyou Chen"
] | The neural attention mechanism plays an important role in many natural language processing applications. In particular, multi-head attention extends single-head attention by allowing a model to jointly attend information from different perspectives. However, without explicit constraining, multi-head attention may suffe... | 2020.emnlp-main.17 | 10.18653/v1/2020.emnlp-main.17 | null | 2009.09364 | title_snapshot |
2020.emnlp-main.18 | KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations | https://aclanthology.org/2020.emnlp-main.18/ | [
"Fabio Massimo Zanzotto",
"Andrea Santilli",
"Leonardo Ranaldi",
"Dario Onorati",
"Pierfrancesco Tommasino",
"Francesca Fallucchi"
] | Syntactic parsers have dominated natural language understanding for decades. Yet, their syntactic interpretations are losing centrality in downstream tasks due to the success of large-scale textual representation learners. In this paper, we propose KERMIT (Kernel-inspired Encoder with Recursive Mechanism for Interpreta... | 2020.emnlp-main.18 | 10.18653/v1/2020.emnlp-main.18 | null | null | null |
2020.emnlp-main.19 | ETC: Encoding Long and Structured Inputs in Transformers | https://aclanthology.org/2020.emnlp-main.19/ | [
"Joshua Ainslie",
"Santiago Ontañón",
"Chris Alberti",
"Vaclav Cvicek",
"Zachary Fisher",
"Philip Pham",
"Anirudh Ravula",
"Sumit Sanghai",
"Qifan Wang",
"Li Yang"
] | Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks. 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 st... | 2020.emnlp-main.19 | 10.18653/v1/2020.emnlp-main.19 | null | 2004.08483 | title_snapshot |
2020.emnlp-main.20 | Pre-Training Transformers as Energy-Based Cloze Models | https://aclanthology.org/2020.emnlp-main.20/ | [
"Kevin Clark",
"Minh-Thang Luong",
"Quoc Le",
"Christopher D. Manning"
] | We introduce Electric, an energy-based cloze model for representation learning over text. Like BERT, it is a conditional generative model of tokens given their contexts. However, Electric does not use masking or output a full distribution over tokens that could occur in a context. Instead, it assigns a scalar energy sc... | 2020.emnlp-main.20 | 10.18653/v1/2020.emnlp-main.20 | null | 2012.08561 | title_snapshot |
2020.emnlp-main.21 | Calibration of Pre-trained Transformers | https://aclanthology.org/2020.emnlp-main.21/ | [
"Shrey Desai",
"Greg Durrett"
] | Pre-trained Transformers are now ubiquitous in natural language processing, but despite their high end-task performance, little is known empirically about whether they are calibrated. Specifically, do these models’ posterior probabilities provide an accurate empirical measure of how likely the model is to be correct on... | 2020.emnlp-main.21 | 10.18653/v1/2020.emnlp-main.21 | null | 2003.07892 | title_snapshot |
2020.emnlp-main.22 | Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding | https://aclanthology.org/2020.emnlp-main.22/ | [
"Jiaming Shen",
"Heng Ji",
"Jiawei Han"
] | Linguistic steganography studies how to hide secret messages in natural language cover texts. Traditional methods aim to transform a secret message into an innocent text via lexical substitution or syntactical modification. Recently, advances in neural language models (LMs) enable us to directly generate cover text con... | 2020.emnlp-main.22 | 10.18653/v1/2020.emnlp-main.22 | null | 2010.00677 | title_snapshot |
2020.emnlp-main.23 | Multi-Dimensional Gender Bias Classification | https://aclanthology.org/2020.emnlp-main.23/ | [
"Emily Dinan",
"Angela Fan",
"Ledell Wu",
"Jason Weston",
"Douwe Kiela",
"Adina Williams"
] | Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a novel, general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gende... | 2020.emnlp-main.23 | 10.18653/v1/2020.emnlp-main.23 | null | 2005.00614 | title_snapshot |
2020.emnlp-main.24 | FIND: Human-in-the-Loop Debugging Deep Text Classifiers | https://aclanthology.org/2020.emnlp-main.24/ | [
"Piyawat Lertvittayakumjorn",
"Lucia Specia",
"Francesca Toni"
] | Since obtaining a perfect training dataset (i.e., a dataset which is considerably large, unbiased, and well-representative of unseen cases) is hardly possible, many real-world text classifiers are trained on the available, yet imperfect, datasets. These classifiers are thus likely to have undesirable properties. For in... | 2020.emnlp-main.24 | 10.18653/v1/2020.emnlp-main.24 | null | 2010.04987 | title_snapshot |
2020.emnlp-main.25 | Conversational Document Prediction to Assist Customer Care Agents | https://aclanthology.org/2020.emnlp-main.25/ | [
"Jatin Ganhotra",
"Haggai Roitman",
"Doron Cohen",
"Nathaniel Mills",
"Chulaka Gunasekara",
"Yosi Mass",
"Sachindra Joshi",
"Luis Lastras",
"David Konopnicki"
] | A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users’ needs. We study the task of predicting the documents that customer care agents can use to facilitate users’ needs. We also introduce a new public dataset which supports the aforementioned problem... | 2020.emnlp-main.25 | 10.18653/v1/2020.emnlp-main.25 | null | 2010.02305 | title_snapshot |
2020.emnlp-main.26 | Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLU | https://aclanthology.org/2020.emnlp-main.26/ | [
"Brielen Madureira",
"David Schlangen"
] | While humans process language incrementally, the best language encoders currently used in NLP do not. Both bidirectional LSTMs and Transformers assume that the sequence that is to be encoded is available in full, to be processed either forwards and backwards (BiLSTMs) or as a whole (Transformers). We investigate how th... | 2020.emnlp-main.26 | 10.18653/v1/2020.emnlp-main.26 | null | 2010.05330 | title_snapshot |
2020.emnlp-main.27 | Augmented Natural Language for Generative Sequence Labeling | https://aclanthology.org/2020.emnlp-main.27/ | [
"Ben Athiwaratkun",
"Cicero Nogueira dos Santos",
"Jason Krone",
"Bing Xiang"
] | We propose a generative framework for joint sequence labeling and sentence-level classification. Our model performs multiple sequence labeling tasks at once using a single, shared natural language output space. Unlike prior discriminative methods, our model naturally incorporates label semantics and shares knowledge ac... | 2020.emnlp-main.27 | 10.18653/v1/2020.emnlp-main.27 | null | 2009.13272 | title_snapshot |
2020.emnlp-main.28 | Dialogue Response Ranking Training with Large-Scale Human Feedback Data | https://aclanthology.org/2020.emnlp-main.28/ | [
"Xiang Gao",
"Yizhe Zhang",
"Michel Galley",
"Chris Brockett",
"Bill Dolan"
] | Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning more followup interactions. Current conversational models are increasingly capable of producing turns that are context-relevant, but in order... | 2020.emnlp-main.28 | 10.18653/v1/2020.emnlp-main.28 | null | 2009.06978 | title_snapshot |
2020.emnlp-main.29 | Semantic Evaluation for Text-to-SQL with Distilled Test Suites | https://aclanthology.org/2020.emnlp-main.29/ | [
"Ruiqi Zhong",
"Tao Yu",
"Dan Klein"
] | We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models. Our method distills a small test suite of databases that achieves high code coverage for the gold query from a large number of randomly generated databases. At evaluation time, it computes the denotation accuracy of the predicted qu... | 2020.emnlp-main.29 | 10.18653/v1/2020.emnlp-main.29 | null | 2010.02840 | title_snapshot |
2020.emnlp-main.30 | Cross-Thought for Sentence Encoder Pre-training | https://aclanthology.org/2020.emnlp-main.30/ | [
"Shuohang Wang",
"Yuwei Fang",
"Siqi Sun",
"Zhe Gan",
"Yu Cheng",
"Jingjing Liu",
"Jing Jiang"
] | 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. Instead of using the original signals of full sentences, we train a Transformer-based sequence encoder over a l... | 2020.emnlp-main.30 | 10.18653/v1/2020.emnlp-main.30 | null | 2010.03652 | title_snapshot |
2020.emnlp-main.31 | AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data | https://aclanthology.org/2020.emnlp-main.31/ | [
"Silei Xu",
"Sina Semnani",
"Giovanni Campagna",
"Monica Lam"
] | We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort. Given a database schema and its data, AutoQA automatically generates a large set of high-quality questions for training that covers different database operations. It uses automatic paraph... | 2020.emnlp-main.31 | 10.18653/v1/2020.emnlp-main.31 | null | 2010.04806 | title_snapshot |
2020.emnlp-main.32 | A Spectral Method for Unsupervised Multi-Document Summarization | https://aclanthology.org/2020.emnlp-main.32/ | [
"Kexiang Wang",
"Baobao Chang",
"Zhifang Sui"
] | Multi-document summarization (MDS) aims at producing a good-quality summary for several related documents. 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. Here spectral impact considers the perturbation to the... | 2020.emnlp-main.32 | 10.18653/v1/2020.emnlp-main.32 | null | null | null |
2020.emnlp-main.33 | What Have We Achieved on Text Summarization? | https://aclanthology.org/2020.emnlp-main.33/ | [
"Dandan Huang",
"Leyang Cui",
"Sen Yang",
"Guangsheng Bao",
"Kun Wang",
"Jun Xie",
"Yue Zhang"
] | Deep learning has led to significant improvement in text summarization with various methods investigated and improved ROUGE scores reported over the years. However, gaps still exist between summaries produced by automatic summarizers and human professionals. Aiming to gain more understanding of summarization systems wi... | 2020.emnlp-main.33 | 10.18653/v1/2020.emnlp-main.33 | null | 2010.04529 | title_snapshot |
2020.emnlp-main.34 | Q-learning with Language Model for Edit-based Unsupervised Summarization | https://aclanthology.org/2020.emnlp-main.34/ | [
"Ryosuke Kohita",
"Akifumi Wachi",
"Yang Zhao",
"Ryuki Tachibana"
] | Unsupervised methods are promising for abstractive textsummarization in that the parallel corpora is not required. However, their performance is still far from being satisfied, therefore research on promising solutions is on-going. In this paper, we propose a new approach based on Q-learning with an edit-based summariz... | 2020.emnlp-main.34 | 10.18653/v1/2020.emnlp-main.34 | null | 2010.04379 | title_snapshot |
2020.emnlp-main.35 | Friendly Topic Assistant for Transformer Based Abstractive Summarization | https://aclanthology.org/2020.emnlp-main.35/ | [
"Zhengjue Wang",
"Zhibin Duan",
"Hao Zhang",
"Chaojie Wang",
"Long Tian",
"Bo Chen",
"Mingyuan Zhou"
] | Abstractive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance. Compared with Transformers, topic models are better at learning explicit document semantics, and hence could be integ... | 2020.emnlp-main.35 | 10.18653/v1/2020.emnlp-main.35 | null | null | null |
2020.emnlp-main.36 | Contrastive Distillation on Intermediate Representations for Language Model Compression | https://aclanthology.org/2020.emnlp-main.36/ | [
"Siqi Sun",
"Zhe Gan",
"Yuwei Fang",
"Yu Cheng",
"Shuohang Wang",
"Jingjing Liu"
] | Existing language model compression methods mostly use a simple L_2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one. Although widely used, this objective by design assumes that all the dimensions of hidden representations are independent, failing to capture important... | 2020.emnlp-main.36 | 10.18653/v1/2020.emnlp-main.36 | null | 2009.14167 | title_snapshot |
2020.emnlp-main.37 | TernaryBERT: Distillation-aware Ultra-low Bit BERT | https://aclanthology.org/2020.emnlp-main.37/ | [
"Wei Zhang",
"Lu Hou",
"Yichun Yin",
"Lifeng Shang",
"Xiao Chen",
"Xin Jiang",
"Qun Liu"
] | Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks. However, these models are both computation and memory expensive, hindering their deployment to resource-constrained devices. In this work, we propose TernaryBERT, which ternarizes the weights ... | 2020.emnlp-main.37 | 10.18653/v1/2020.emnlp-main.37 | null | 2009.12812 | title_snapshot |
2020.emnlp-main.38 | Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks | https://aclanthology.org/2020.emnlp-main.38/ | [
"Trapit Bansal",
"Rishikesh Jha",
"Tsendsuren Munkhdalai",
"Andrew McCallum"
] | Self-supervised pre-training of transformer models has revolutionized NLP applications. Such pre-training with language modeling objectives provides a useful initial point for parameters that generalize well to new tasks with fine-tuning. However, fine-tuning is still data inefficient — when there are few labeled examp... | 2020.emnlp-main.38 | 10.18653/v1/2020.emnlp-main.38 | null | 2009.08445 | title_snapshot |
2020.emnlp-main.39 | Efficient Meta Lifelong-Learning with Limited Memory | https://aclanthology.org/2020.emnlp-main.39/ | [
"Zirui Wang",
"Sanket Vaibhav Mehta",
"Barnabas Poczos",
"Jaime Carbonell"
] | Current natural language processing models work well on a single task, yet they often fail to continuously learn new tasks without forgetting previous ones as they are re-trained throughout their lifetime, a challenge known as lifelong learning. State-of-the-art lifelong language learning methods store past examples in... | 2020.emnlp-main.39 | 10.18653/v1/2020.emnlp-main.39 | null | 2010.02500 | title_snapshot |
2020.emnlp-main.40 | Don’t Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings | https://aclanthology.org/2020.emnlp-main.40/ | [
"Phillip Keung",
"Yichao Lu",
"Julian Salazar",
"Vikas Bhardwaj"
] | Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning, where multilingual BERT is fine-tuned on one source language and evaluated on a different target language. However, published results for mBERT zero-shot accuracy vary as much as 17 points on ... | 2020.emnlp-main.40 | 10.18653/v1/2020.emnlp-main.40 | null | 2004.15001 | title_snapshot |
2020.emnlp-main.41 | A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT | https://aclanthology.org/2020.emnlp-main.41/ | [
"Masaaki Nagata",
"Katsuki Chousa",
"Masaaki Nishino"
] | We present a novel supervised word alignment method based on cross-language span prediction. We first formalize a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence. Since this step is equivalent to a SQuAD v2.0 style question answering... | 2020.emnlp-main.41 | 10.18653/v1/2020.emnlp-main.41 | null | 2004.14516 | title_snapshot |
2020.emnlp-main.42 | Accurate Word Alignment Induction from Neural Machine Translation | https://aclanthology.org/2020.emnlp-main.42/ | [
"Yun Chen",
"Yang Liu",
"Guanhua Chen",
"Xin Jiang",
"Qun Liu"
] | Despite its original goal to jointly learn to align and translate, prior researches suggest that Transformer captures poor word alignments through its attention mechanism. In this paper, we show that attention weights do capture accurate word alignments and propose two novel word alignment induction methods Shift-Att a... | 2020.emnlp-main.42 | 10.18653/v1/2020.emnlp-main.42 | null | 2004.14837 | title_snapshot |
2020.emnlp-main.43 | ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization | https://aclanthology.org/2020.emnlp-main.43/ | [
"Shiyue Zhang",
"Benjamin Frey",
"Mohit Bansal"
] | Cherokee is a highly endangered Native American language spoken by the Cherokee people. The Cherokee culture is deeply embedded in its language. However, there are approximately only 2,000 fluent first language Cherokee speakers remaining in the world and the number is declining every year. To help save this endangered... | 2020.emnlp-main.43 | 10.18653/v1/2020.emnlp-main.43 | null | 2010.04791 | title_snapshot |
2020.emnlp-main.44 | Unsupervised Discovery of Implicit Gender Bias | https://aclanthology.org/2020.emnlp-main.44/ | [
"Anjalie Field",
"Yulia Tsvetkov"
] | Despite their prevalence in society, social biases are difficult to identify, primarily because human judgements in this domain can be unreliable. 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. Our main chall... | 2020.emnlp-main.44 | 10.18653/v1/2020.emnlp-main.44 | null | 2004.08361 | title_snapshot |
2020.emnlp-main.45 | Condolence and Empathy in Online Communities | https://aclanthology.org/2020.emnlp-main.45/ | [
"Naitian Zhou",
"David Jurgens"
] | Offering condolence is a natural reaction to hearing someone’s distress. Individuals frequently express distress in social media, where some communities can provide support. However, not all condolence is equal—trite responses offer little actual support despite their good intentions. Here, we develop computational too... | 2020.emnlp-main.45 | 10.18653/v1/2020.emnlp-main.45 | null | null | null |
2020.emnlp-main.46 | An Embedding Model for Estimating Legislative Preferences from the Frequency and Sentiment of Tweets | https://aclanthology.org/2020.emnlp-main.46/ | [
"Gregory Spell",
"Brian Guay",
"Sunshine Hillygus",
"Lawrence Carin"
] | Legislator preferences are typically represented as measures of general ideology estimated from roll call votes on legislation, potentially masking important nuances in legislators’ political attitudes. In this paper we introduce a method of measuring more specific legislator attitudes using an alternative expression o... | 2020.emnlp-main.46 | 10.18653/v1/2020.emnlp-main.46 | null | null | null |
2020.emnlp-main.47 | Measuring Information Propagation in Literary Social Networks | https://aclanthology.org/2020.emnlp-main.47/ | [
"Matthew Sims",
"David Bamman"
] | We present the task of modeling information propagation in literature, in which we seek to identify pieces of information passing from character A to character B to character C, only given a description of their activity in text. We describe a new pipeline for measuring information propagation in this domain and publis... | 2020.emnlp-main.47 | 10.18653/v1/2020.emnlp-main.47 | null | 2004.13980 | title_snapshot |
2020.emnlp-main.48 | Social Chemistry 101: Learning to Reason about Social and Moral Norms | https://aclanthology.org/2020.emnlp-main.48/ | [
"Maxwell Forbes",
"Jena D. Hwang",
"Vered Shwartz",
"Maarten Sap",
"Yejin Choi"
] | Social norms—the unspoken commonsense rules about acceptable social behavior—are crucial in understanding the underlying causes and intents of people’s actions in narratives. For example, underlying an action such as “wanting to call cops on my neighbor” are social norms that inform our conduct, such as “It is expected... | 2020.emnlp-main.48 | 10.18653/v1/2020.emnlp-main.48 | null | 2011.00620 | title_snapshot |
2020.emnlp-main.49 | Event Extraction by Answering (Almost) Natural Questions | https://aclanthology.org/2020.emnlp-main.49/ | [
"Xinya Du",
"Claire Cardie"
] | The problem of event extraction requires detecting the event trigger and extracting its corresponding arguments. Existing work in event argument extraction typically relies heavily on entity recognition as a preprocessing/concurrent step, causing the well-known problem of error propagation. To avoid this issue, we intr... | 2020.emnlp-main.49 | 10.18653/v1/2020.emnlp-main.49 | null | 2004.13625 | title_snapshot |
2020.emnlp-main.50 | Connecting the Dots: Event Graph Schema Induction with Path Language Modeling | https://aclanthology.org/2020.emnlp-main.50/ | [
"Manling Li",
"Qi Zeng",
"Ying Lin",
"Kyunghyun Cho",
"Heng Ji",
"Jonathan May",
"Nathanael Chambers",
"Clare Voss"
] | Event schemas can guide our understanding and ability to make predictions with respect to what might happen next. 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. We then introduce Path Language Model, an au... | 2020.emnlp-main.50 | 10.18653/v1/2020.emnlp-main.50 | null | null | null |
2020.emnlp-main.51 | Joint Constrained Learning for Event-Event Relation Extraction | https://aclanthology.org/2020.emnlp-main.51/ | [
"Haoyu Wang",
"Muhao Chen",
"Hongming Zhang",
"Dan Roth"
] | Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. In this process, one can induce event complexes that organize multi-granular events with temporal order and membership relations interweaving among them. Due to the lack of jointly label... | 2020.emnlp-main.51 | 10.18653/v1/2020.emnlp-main.51 | null | 2010.06727 | title_snapshot |
2020.emnlp-main.52 | Incremental Event Detection via Knowledge Consolidation Networks | https://aclanthology.org/2020.emnlp-main.52/ | [
"Pengfei Cao",
"Yubo Chen",
"Jun Zhao",
"Taifeng Wang"
] | Conventional approaches to event detection usually require a fixed set of pre-defined event types. Such a requirement is often challenged in real-world applications, as new events continually occur. Due to huge computation cost and storage budge, it is infeasible to store all previous data and re-train the model with a... | 2020.emnlp-main.52 | 10.18653/v1/2020.emnlp-main.52 | null | null | null |
2020.emnlp-main.53 | Semi-supervised New Event Type Induction and Event Detection | https://aclanthology.org/2020.emnlp-main.53/ | [
"Lifu Huang",
"Heng Ji"
] | Most previous event extraction studies assume a set of target event types and corresponding event annotations are given, which could be very expensive. 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 a... | 2020.emnlp-main.53 | 10.18653/v1/2020.emnlp-main.53 | null | null | null |
2020.emnlp-main.54 | Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph | https://aclanthology.org/2020.emnlp-main.54/ | [
"Haozhe Ji",
"Pei Ke",
"Shaohan Huang",
"Furu Wei",
"Xiaoyan Zhu",
"Minlie Huang"
] | Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. Existing approaches that integrate commonsense knowledge into generative pre-trained language models simply ... | 2020.emnlp-main.54 | 10.18653/v1/2020.emnlp-main.54 | null | 2009.11692 | title_snapshot |
2020.emnlp-main.55 | Reformulating Unsupervised Style Transfer as Paraphrase Generation | https://aclanthology.org/2020.emnlp-main.55/ | [
"Kalpesh Krishna",
"John Wieting",
"Mohit Iyyer"
] | Modern NLP defines the task of style transfer as modifying the style of a given sentence without appreciably changing its semantics, which implies that the outputs of style transfer systems should be paraphrases of their inputs. However, many existing systems purportedly designed for style transfer inherently warp the ... | 2020.emnlp-main.55 | 10.18653/v1/2020.emnlp-main.55 | null | 2010.05700 | title_snapshot |
2020.emnlp-main.56 | De-Biased Court’s View Generation with Causality | https://aclanthology.org/2020.emnlp-main.56/ | [
"Yiquan Wu",
"Kun Kuang",
"Yating Zhang",
"Xiaozhong Liu",
"Changlong Sun",
"Jun Xiao",
"Yueting Zhuang",
"Luo Si",
"Fei Wu"
] | Court’s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation. While prior text-to-text natural language generation (NLG) approaches can be used to address this problem, neglecting the confound... | 2020.emnlp-main.56 | 10.18653/v1/2020.emnlp-main.56 | null | null | null |
2020.emnlp-main.57 | PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation | https://aclanthology.org/2020.emnlp-main.57/ | [
"Xinyu Hua",
"Lu Wang"
] | Pre-trained Transformers have enabled impressive breakthroughs in generating long and fluent text, yet their outputs are often “rambling” without coherently arranged content. In this work, we present a novel content-controlled text generation framework, PAIR, with planning and iterative refinement, which is built upon ... | 2020.emnlp-main.57 | 10.18653/v1/2020.emnlp-main.57 | null | 2010.02301 | title_snapshot |
2020.emnlp-main.58 | Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning | https://aclanthology.org/2020.emnlp-main.58/ | [
"Lianhui Qin",
"Vered Shwartz",
"Peter West",
"Chandra Bhagavatula",
"Jena D. Hwang",
"Ronan Le Bras",
"Antoine Bosselut",
"Yejin Choi"
] | Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future. However, simultaneous incorporation of past and future contexts using generative language models (LMs... | 2020.emnlp-main.58 | 10.18653/v1/2020.emnlp-main.58 | null | 2010.05906 | title_snapshot |
2020.emnlp-main.59 | Where Are You? Localization from Embodied Dialog | https://aclanthology.org/2020.emnlp-main.59/ | [
"Meera Hahn",
"Jacob Krantz",
"Dhruv Batra",
"Devi Parikh",
"James Rehg",
"Stefan Lee",
"Peter Anderson"
] | We present WHERE ARE YOU? (WAY), a dataset of ~6k dialogs in which two humans – an Observer and a Locator – complete a cooperative localization task. The Observer is spawned at random in a 3D environment and can navigate from first-person views while answering questions from the Locator. The Locator must localize the O... | 2020.emnlp-main.59 | 10.18653/v1/2020.emnlp-main.59 | null | 2011.08277 | title_snapshot |
2020.emnlp-main.60 | Learning to Represent Image and Text with Denotation Graph | https://aclanthology.org/2020.emnlp-main.60/ | [
"Bowen Zhang",
"Hexiang Hu",
"Vihan Jain",
"Eugene Ie",
"Fei Sha"
] | Learning to fuse vision and language information and representing them is an important research problem with many applications. Recent progresses have leveraged the ideas of pre-training (from language modeling) and attention layers in Transformers to learn representation from datasets containing images aligned with li... | 2020.emnlp-main.60 | 10.18653/v1/2020.emnlp-main.60 | null | 2010.02949 | title_snapshot |
2020.emnlp-main.61 | Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning | https://aclanthology.org/2020.emnlp-main.61/ | [
"Zhiyuan Fang",
"Tejas Gokhale",
"Pratyay Banerjee",
"Chitta Baral",
"Yezhou Yang"
] | Captioning is a crucial and challenging task for video understanding. In videos that involve active agents such as humans, the agent’s actions can bring about myriad changes in the scene. Observable changes such as movements, manipulations, and transformations of the objects in the scene, are reflected in conventional ... | 2020.emnlp-main.61 | 10.18653/v1/2020.emnlp-main.61 | null | 2003.05162 | title_snapshot |
2020.emnlp-main.62 | Does my multimodal model learn cross-modal interactions? It’s harder to tell than you might think! | https://aclanthology.org/2020.emnlp-main.62/ | [
"Jack Hessel",
"Lillian Lee"
] | Modeling expressive cross-modal interactions seems crucial in multimodal tasks, such as visual question answering. However, sometimes high-performing black-box algorithms turn out to be mostly exploiting unimodal signals in the data. We propose a new diagnostic tool, empirical multimodally-additive function projection ... | 2020.emnlp-main.62 | 10.18653/v1/2020.emnlp-main.62 | null | 2010.06572 | title_snapshot |
2020.emnlp-main.63 | MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering | https://aclanthology.org/2020.emnlp-main.63/ | [
"Tejas Gokhale",
"Pratyay Banerjee",
"Chitta Baral",
"Yezhou Yang"
] | While progress has been made on the visual question answering leaderboards, models often utilize spurious correlations and priors in datasets under the i.i.d. setting. As such, evaluation on out-of-distribution (OOD) test samples has emerged as a proxy for generalization. In this paper, we present MUTANT, a training pa... | 2020.emnlp-main.63 | 10.18653/v1/2020.emnlp-main.63 | null | 2009.08566 | title_snapshot |
2020.emnlp-main.64 | Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning | https://aclanthology.org/2020.emnlp-main.64/ | [
"Haochen Liu",
"Wentao Wang",
"Yiqi Wang",
"Hui Liu",
"Zitao Liu",
"Jiliang Tang"
] | Dialogue systems play an increasingly important role in various aspects of our daily life. It is evident from recent research that dialogue systems trained on human conversation data are biased. In particular, they can produce responses that reflect people’s gender prejudice. Many debiasing methods have been developed ... | 2020.emnlp-main.64 | 10.18653/v1/2020.emnlp-main.64 | null | 2009.13028 | title_snapshot |
2020.emnlp-main.65 | Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness | https://aclanthology.org/2020.emnlp-main.65/ | [
"Hyunwoo Kim",
"Byeongchang Kim",
"Gunhee Kim"
] | We explore the task of improving persona consistency of dialogue agents. Recent models tackling consistency often train with additional Natural Language Inference (NLI) labels or attach trained extra modules to the generative agent for maintaining consistency. However, such additional labels and training can be demandi... | 2020.emnlp-main.65 | 10.18653/v1/2020.emnlp-main.65 | null | 2004.05816 | title_snapshot |
2020.emnlp-main.66 | TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue | https://aclanthology.org/2020.emnlp-main.66/ | [
"Chien-Sheng Wu",
"Steven C.H. Hoi",
"Richard Socher",
"Caiming Xiong"
] | The underlying difference of linguistic patterns between general text and task-oriented dialogue makes existing pre-trained language models less useful in practice. In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling. To better model dialogue behavior during pre-... | 2020.emnlp-main.66 | 10.18653/v1/2020.emnlp-main.66 | null | 2004.06871 | title_snapshot |
2020.emnlp-main.67 | RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling | https://aclanthology.org/2020.emnlp-main.67/ | [
"Jun Quan",
"Shian Zhang",
"Qian Cao",
"Zizhong Li",
"Deyi Xiong"
] | 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. RiSAWOZ contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialog... | 2020.emnlp-main.67 | 10.18653/v1/2020.emnlp-main.67 | null | 2010.08738 | title_snapshot |
2020.emnlp-main.68 | Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness | https://aclanthology.org/2020.emnlp-main.68/ | [
"Reina Akama",
"Sho Yokoi",
"Jun Suzuki",
"Kentaro Inui"
] | Large-scale dialogue datasets have recently become available for training neural dialogue agents. However, these datasets have been reported to contain a non-negligible number of unacceptable utterance pairs. In this paper, we propose a method for scoring the quality of utterance pairs in terms of their connectivity an... | 2020.emnlp-main.68 | 10.18653/v1/2020.emnlp-main.68 | null | 2004.14008 | title_snapshot |
2020.emnlp-main.69 | Latent Geographical Factors for Analyzing the Evolution of Dialects in Contact | https://aclanthology.org/2020.emnlp-main.69/ | [
"Yugo Murawaki"
] | Analyzing the evolution of dialects remains a challenging problem because contact phenomena hinder the application of the standard tree model. Previous statistical approaches to this problem resort to admixture analysis, where each dialect is seen as a mixture of latent ancestral populations. However, such ancestral po... | 2020.emnlp-main.69 | 10.18653/v1/2020.emnlp-main.69 | null | null | null |
2020.emnlp-main.70 | Predicting Reference: What do Language Models Learn about Discourse Models? | https://aclanthology.org/2020.emnlp-main.70/ | [
"Shiva Upadhye",
"Leon Bergen",
"Andrew Kehler"
] | Whereas there is a growing literature that probes neural language models to assess the degree to which they have latently acquired grammatical knowledge, little if any research has investigated their acquisition of discourse modeling ability. We address this question by drawing on a rich psycholinguistic literature tha... | 2020.emnlp-main.70 | 10.18653/v1/2020.emnlp-main.70 | null | null | null |
2020.emnlp-main.71 | Word class flexibility: A deep contextualized approach | https://aclanthology.org/2020.emnlp-main.71/ | [
"Bai Li",
"Guillaume Thomas",
"Yang Xu",
"Frank Rudzicz"
] | Word class flexibility refers to the phenomenon whereby a single word form is used across different grammatical categories. Extensive work in linguistic typology has sought to characterize word class flexibility across languages, but quantifying this phenomenon accurately and at scale has been fraught with difficulties... | 2020.emnlp-main.71 | 10.18653/v1/2020.emnlp-main.71 | null | 2009.09241 | title_snapshot |
2020.emnlp-main.72 | Shallow-to-Deep Training for Neural Machine Translation | https://aclanthology.org/2020.emnlp-main.72/ | [
"Bei Li",
"Ziyang Wang",
"Hui Liu",
"Yufan Jiang",
"Quan Du",
"Tong Xiao",
"Huizhen Wang",
"Jingbo Zhu"
] | Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but training an extremely deep encoder is time consuming. Moreover, why deep models help NMT is an open question. In this paper, we investigate the behavior of a well-tuned deep Transformer system. We find that stackin... | 2020.emnlp-main.72 | 10.18653/v1/2020.emnlp-main.72 | null | 2010.03737 | title_snapshot |
2020.emnlp-main.73 | Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation | https://aclanthology.org/2020.emnlp-main.73/ | [
"Jason Lee",
"Raphael Shu",
"Kyunghyun Cho"
] | We propose an efficient inference procedure for non-autoregressive machine translation that iteratively refines translation purely in the continuous space. Given a continuous latent variable model for machine translation (Shu et al., 2020), we train an inference network to approximate the gradient of the marginal log p... | 2020.emnlp-main.73 | 10.18653/v1/2020.emnlp-main.73 | null | 2009.07177 | title_snapshot |
2020.emnlp-main.74 | Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers | https://aclanthology.org/2020.emnlp-main.74/ | [
"Yimeng Wu",
"Peyman Passban",
"Mehdi Rezagholizadeh",
"Qun Liu"
] | With the growth of computing power neural machine translation (NMT) models also grow accordingly and become better. However, they also become harder to deploy on edge devices due to memory constraints. To cope with this problem, a common practice is to distill knowledge from a large and accurately-trained teacher netwo... | 2020.emnlp-main.74 | 10.18653/v1/2020.emnlp-main.74 | null | 2010.03034 | title_snapshot |
2020.emnlp-main.75 | Multi-task Learning for Multilingual Neural Machine Translation | https://aclanthology.org/2020.emnlp-main.75/ | [
"Yiren Wang",
"ChengXiang Zhai",
"Hany Hassan"
] | While monolingual data has been shown to be useful in improving bilingual neural machine translation (NMT), effectively and efficiently leveraging monolingual data for Multilingual NMT (MNMT) systems is a less explored area. In this work, we propose a multi-task learning (MTL) framework that jointly trains the model wi... | 2020.emnlp-main.75 | 10.18653/v1/2020.emnlp-main.75 | null | 2010.02523 | title_snapshot |
2020.emnlp-main.76 | Token-level Adaptive Training for Neural Machine Translation | https://aclanthology.org/2020.emnlp-main.76/ | [
"Shuhao Gu",
"Jinchao Zhang",
"Fandong Meng",
"Yang Feng",
"Wanying Xie",
"Jie Zhou",
"Dong Yu"
] | There exists a token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to different learning difficulties for tokens in Neural Machine Translation (NMT). The vanilla NMT model usually adopts trivial equal-weighted objectives for target tokens with different freq... | 2020.emnlp-main.76 | 10.18653/v1/2020.emnlp-main.76 | null | 2010.04380 | title_snapshot |
2020.emnlp-main.77 | Multi-Unit Transformers for Neural Machine Translation | https://aclanthology.org/2020.emnlp-main.77/ | [
"Jianhao Yan",
"Fandong Meng",
"Jie Zhou"
] | Transformer models achieve remarkable success in Neural Machine Translation. Many efforts have been devoted to deepening the Transformer by stacking several units (i.e., a combination of Multihead Attentions and FFN) in a cascade, while the investigation over multiple parallel units draws little attention. In this pape... | 2020.emnlp-main.77 | 10.18653/v1/2020.emnlp-main.77 | null | 2010.10743 | title_snapshot |
2020.emnlp-main.78 | On the Sparsity of Neural Machine Translation Models | https://aclanthology.org/2020.emnlp-main.78/ | [
"Yong Wang",
"Longyue Wang",
"Victor Li",
"Zhaopeng Tu"
] | Modern neural machine translation (NMT) models employ a large number of parameters, which leads to serious over-parameterization and typically causes the underutilization of computational resources. In response to this problem, we empirically investigate whether the redundant parameters can be reused to achieve better ... | 2020.emnlp-main.78 | 10.18653/v1/2020.emnlp-main.78 | null | 2010.02646 | title_snapshot |
2020.emnlp-main.79 | Incorporating a Local Translation Mechanism into Non-autoregressive Translation | https://aclanthology.org/2020.emnlp-main.79/ | [
"Xiang Kong",
"Zhisong Zhang",
"Eduard Hovy"
] | 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. Specifically, for each target decoding position, instead of only one token, we predict a short sequence of tokens in an autoregre... | 2020.emnlp-main.79 | 10.18653/v1/2020.emnlp-main.79 | null | 2011.06132 | title_snapshot |
2020.emnlp-main.80 | Self-Paced Learning for Neural Machine Translation | https://aclanthology.org/2020.emnlp-main.80/ | [
"Yu Wan",
"Baosong Yang",
"Derek F. Wong",
"Yikai Zhou",
"Lidia S. Chao",
"Haibo Zhang",
"Boxing Chen"
] | Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans. Nevertheless, achievements of such kind of curriculum learning rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word ... | 2020.emnlp-main.80 | 10.18653/v1/2020.emnlp-main.80 | null | 2010.04505 | title_snapshot |
2020.emnlp-main.81 | Long-Short Term Masking Transformer: A Simple but Effective Baseline for Document-level Neural Machine Translation | https://aclanthology.org/2020.emnlp-main.81/ | [
"Pei Zhang",
"Boxing Chen",
"Niyu Ge",
"Kai Fan"
] | Many document-level neural machine translation (NMT) systems have explored the utility of context-aware architecture, usually requiring an increasing number of parameters and computational complexity. However, few attention is paid to the baseline model. In this paper, we research extensively the pros and cons of the s... | 2020.emnlp-main.81 | 10.18653/v1/2020.emnlp-main.81 | null | 2009.09127 | title_snapshot |
2020.emnlp-main.82 | Generating Diverse Translation from Model Distribution with Dropout | https://aclanthology.org/2020.emnlp-main.82/ | [
"Xuanfu Wu",
"Yang Feng",
"Chenze Shao"
] | Despite the improvement of translation quality, neural machine translation (NMT) often suffers from the lack of diversity in its generation. 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. The p... | 2020.emnlp-main.82 | 10.18653/v1/2020.emnlp-main.82 | null | 2010.08178 | title_snapshot |
2020.emnlp-main.83 | Non-Autoregressive Machine Translation with Latent Alignments | https://aclanthology.org/2020.emnlp-main.83/ | [
"Chitwan Saharia",
"William Chan",
"Saurabh Saxena",
"Mohammad Norouzi"
] | This paper presents two strong methods, CTC and Imputer, for non-autoregressive machine translation that model latent alignments with dynamic programming. We revisit CTC for machine translation and demonstrate that a simple CTC model can achieve state-of-the-art for single-step non-autoregressive machine translation, c... | 2020.emnlp-main.83 | 10.18653/v1/2020.emnlp-main.83 | null | 2004.07437 | title_snapshot |
2020.emnlp-main.84 | Look at the First Sentence: Position Bias in Question Answering | https://aclanthology.org/2020.emnlp-main.84/ | [
"Miyoung Ko",
"Jinhyuk Lee",
"Hyunjae Kim",
"Gangwoo Kim",
"Jaewoo Kang"
] | Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g... | 2020.emnlp-main.84 | 10.18653/v1/2020.emnlp-main.84 | null | 2004.14602 | title_snapshot |
2020.emnlp-main.85 | ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning | https://aclanthology.org/2020.emnlp-main.85/ | [
"Michael Boratko",
"Xiang Lorraine Li",
"Tim O’Gorman",
"Rajarshi Das",
"Dan Le",
"Andrew McCallum"
] | Given questions regarding some prototypical situation — such as Name something that people usually do before they leave the house for work? — a human can easily answer them via acquired experiences. There can be multiple right answers for such questions, with some more common for a situation than others. This paper int... | 2020.emnlp-main.85 | 10.18653/v1/2020.emnlp-main.85 | null | 2005.00771 | title_snapshot |
2020.emnlp-main.86 | IIRC: A Dataset of Incomplete Information Reading Comprehension Questions | https://aclanthology.org/2020.emnlp-main.86/ | [
"James Ferguson",
"Matt Gardner",
"Hannaneh Hajishirzi",
"Tushar Khot",
"Pradeep Dasigi"
] | Humans often have to read multiple documents to address their information needs. However, most existing reading comprehension (RC) tasks only focus on questions for which the contexts provide all the information required to answer them, thus not evaluating a system’s performance at identifying a potential lack of suffi... | 2020.emnlp-main.86 | 10.18653/v1/2020.emnlp-main.86 | null | 2011.07127 | title_snapshot |
2020.emnlp-main.87 | Unsupervised Adaptation of Question Answering Systems via Generative Self-training | https://aclanthology.org/2020.emnlp-main.87/ | [
"Steven Rennie",
"Etienne Marcheret",
"Neil Mallinar",
"David Nahamoo",
"Vaibhava Goel"
] | BERT-era question answering systems have recently achieved impressive performance on several question-answering (QA) tasks. These systems are based on representations that have been pre-trained on self-supervised tasks such as word masking and sentence entailment, using massive amounts of data. Nevertheless, additional... | 2020.emnlp-main.87 | 10.18653/v1/2020.emnlp-main.87 | null | null | null |
2020.emnlp-main.88 | TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions | https://aclanthology.org/2020.emnlp-main.88/ | [
"Qiang Ning",
"Hao Wu",
"Rujun Han",
"Nanyun Peng",
"Matt Gardner",
"Dan Roth"
] | A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated. However, current machine reading comprehension benchmarks have practically no questions that test temporal phenomena, so systems train... | 2020.emnlp-main.88 | 10.18653/v1/2020.emnlp-main.88 | null | 2005.00242 | title_snapshot |
2020.emnlp-main.89 | ToTTo: A Controlled Table-To-Text Generation Dataset | https://aclanthology.org/2020.emnlp-main.89/ | [
"Ankur Parikh",
"Xuezhi Wang",
"Sebastian Gehrmann",
"Manaal Faruqui",
"Bhuwan Dhingra",
"Diyi Yang",
"Dipanjan Das"
] | 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. To obtain generated targets that are natural but also faithful to the source ta... | 2020.emnlp-main.89 | 10.18653/v1/2020.emnlp-main.89 | null | 2004.14373 | title_snapshot |
2020.emnlp-main.90 | ENT-DESC: Entity Description Generation by Exploring Knowledge Graph | https://aclanthology.org/2020.emnlp-main.90/ | [
"Liying Cheng",
"Dekun Wu",
"Lidong Bing",
"Yan Zhang",
"Zhanming Jie",
"Wei Lu",
"Luo Si"
] | Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WIKIBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text... | 2020.emnlp-main.90 | 10.18653/v1/2020.emnlp-main.90 | null | 2004.14813 | title_snapshot |
2020.emnlp-main.91 | Small but Mighty: New Benchmarks for Split and Rephrase | https://aclanthology.org/2020.emnlp-main.91/ | [
"Li Zhang",
"Huaiyu Zhu",
"Siddhartha Brahma",
"Yunyao Li"
] | Split and Rephrase is a text simplification task of rewriting a complex sentence into simpler ones. As a relatively new task, it is paramount to ensure the soundness of its evaluation benchmark and metric. We find that the widely used benchmark dataset universally contains easily exploitable syntactic cues caused by it... | 2020.emnlp-main.91 | 10.18653/v1/2020.emnlp-main.91 | null | 2009.08560 | title_snapshot |
2020.emnlp-main.92 | Online Back-Parsing for AMR-to-Text Generation | https://aclanthology.org/2020.emnlp-main.92/ | [
"Xuefeng Bai",
"Linfeng Song",
"Yue Zhang"
] | AMR-to-text generation aims to recover a text containing the same meaning as an input AMR graph. Current research develops increasingly powerful graph encoders to better represent AMR graphs, with decoders based on standard language modeling being used to generate outputs. We propose a decoder that back predicts projec... | 2020.emnlp-main.92 | 10.18653/v1/2020.emnlp-main.92 | null | 2010.04520 | title_snapshot |
2020.emnlp-main.93 | Reading Between the Lines: Exploring Infilling in Visual Narratives | https://aclanthology.org/2020.emnlp-main.93/ | [
"Khyathi Raghavi Chandu",
"Ruo-Ping Dong",
"Alan W Black"
] | Generating long form narratives such as stories and procedures from multiple modalities has been a long standing dream for artificial intelligence. In this regard, there is often crucial subtext that is derived from the surrounding contexts. The general seq2seq training methods render the models shorthanded while attem... | 2020.emnlp-main.93 | 10.18653/v1/2020.emnlp-main.93 | null | 2010.13944 | title_snapshot |
2020.emnlp-main.94 | Acrostic Poem Generation | https://aclanthology.org/2020.emnlp-main.94/ | [
"Rajat Agarwal",
"Katharina Kann"
] | We propose a new task in the area of computational creativity: acrostic poem generation in English. Acrostic poems are poems that contain a hidden message; typically, the first letter of each line spells out a word or short phrase. We define the task as a generation task with multiple constraints: given an input word, ... | 2020.emnlp-main.94 | 10.18653/v1/2020.emnlp-main.94 | null | 2010.02239 | title_snapshot |
2020.emnlp-main.95 | Local Additivity Based Data Augmentation for Semi-supervised NER | https://aclanthology.org/2020.emnlp-main.95/ | [
"Jiaao Chen",
"Zhenghui Wang",
"Ran Tian",
"Zichao Yang",
"Diyi Yang"
] | Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. 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... | 2020.emnlp-main.95 | 10.18653/v1/2020.emnlp-main.95 | null | 2010.01677 | title_snapshot |
2020.emnlp-main.96 | Grounded Compositional Outputs for Adaptive Language Modeling | https://aclanthology.org/2020.emnlp-main.96/ | [
"Nikolaos Pappas",
"Phoebe Mulcaire",
"Noah A. Smith"
] | Language models have emerged as a central component across NLP, and a great deal of progress depends on the ability to cheaply adapt them (e.g., through finetuning) to new domains and tasks. A language model’s vocabulary—typically selected before training and permanently fixed later—affects its size and is part of what... | 2020.emnlp-main.96 | 10.18653/v1/2020.emnlp-main.96 | null | 2009.11523 | title_snapshot |
2020.emnlp-main.97 | SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness | https://aclanthology.org/2020.emnlp-main.97/ | [
"Nathan Ng",
"Kyunghyun Cho",
"Marzyeh Ghassemi"
] | Models that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples. Data augmentation is a common method used to prevent overfitting and improve OOD generalization. However, in natural language, it is difficult to generate new examples that stay on the underlying data manifold. We in... | 2020.emnlp-main.97 | 10.18653/v1/2020.emnlp-main.97 | null | 2009.10195 | title_snapshot |
2020.emnlp-main.98 | SetConv: A New Approach for Learning from Imbalanced Data | https://aclanthology.org/2020.emnlp-main.98/ | [
"Yang Gao",
"Yi-Fan Li",
"Yu Lin",
"Charu Aggarwal",
"Latifur Khan"
] | For many real-world classification problems, e.g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (IR) is high. To address this problem, we propose a set convolution (SetConv) operation and an episodic training strategy to extract a single... | 2020.emnlp-main.98 | 10.18653/v1/2020.emnlp-main.98 | null | 2104.06313 | title_snapshot |
2020.emnlp-main.99 | Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering | https://aclanthology.org/2020.emnlp-main.99/ | [
"Yanlin Feng",
"Xinyue Chen",
"Bill Yuchen Lin",
"Peifeng Wang",
"Jun Yan",
"Xiang Ren"
] | Existing work on augmenting question answering (QA) models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model’s prediction rationale. In this paper, we propose a novel knowledge-aware approach that equips pre-trained language mo... | 2020.emnlp-main.99 | 10.18653/v1/2020.emnlp-main.99 | null | 2005.00646 | title_snapshot |
2020.emnlp-main.100 | Improving Bilingual Lexicon Induction for Low Frequency Words | https://aclanthology.org/2020.emnlp-main.100/ | [
"Jiaji Huang",
"Xingyu Cai",
"Kenneth Church"
] | This paper designs a Monolingual Lexicon Induction task and observes that two factors accompany the degraded accuracy of bilingual lexicon induction for rare words. First, a diminishing margin between similarities in low frequency regime, and secondly, exacerbated hubness at low frequency. Based on the observation, we ... | 2020.emnlp-main.100 | 10.18653/v1/2020.emnlp-main.100 | null | null | null |
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