Viewing 21-40 of 302 papers
Clear all
    • EMNLP 2019
      Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi
      Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly. In this paper, we introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based…  (More)
    • EMNLP 2019
      David Wadden, Ulme Wennberg, Yi Luan, Hannaneh Hajishirzi
      We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our framework (called DyGIE++) accomplishes all tasks by enumerating, refining, and scoring text spans designed to capture…  (More)
    • EMNLP 2019
      Bhavana Dalvi Mishra, Niket Tandon, Antoine Bosselut, Wen-tau Yih, Peter Clark
      Our goal is to better comprehend procedural text, e.g., a paragraph about photosynthesis, by not only predicting what happens, but why some actions need to happen before others. Our approach builds on a prior process comprehension framework for predicting actions' effects, to also identify…  (More)
    • EMNLP 2019
      Iz Beltagy, Kyle Lo, Arman Cohan
      Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised…  (More)
    • EMNLP 2019
      Jaemin Cho, Minjoon Seo, Hannaneh Hajishirzi
      Generating diverse sequences is important in many NLP applications such as question generation or summarization that exhibit semantically one-to-many relationships between source and the target sequences. We present a method to explicitly separate diversification from generation using a general…  (More)
    • EMNLP 2019
      Niket Tandon, Bhavana Dalvi Mishra, Keisuke Sakaguchi, Antoine Bosselut, Peter Clark
      We introduce WIQA, the first large-scale dataset of "What if..." questions over procedural text. WIQA contains three parts: a collection of paragraphs each describing a process, e.g., beach erosion; a set of crowdsourced influence graphs for each paragraph, describing how one change affects another…  (More)
    • EMNLP 2019
      Peter West, Ari Holtzman, Jan Buys, Yejin Choi
      The principle of the Information Bottleneck (Tishby et al. 1999) is to produce a summary of information X optimized to predict some other relevant information Y. In this paper, we propose a novel approach to unsupervised sentence summarization by mapping the Information Bottleneck principle to a…  (More)
    • EMNLP 2019
      Ben Bogin, Matt Gardner, Jonathan Berant
      State-of-the-art semantic parsers rely on auto-regressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zero-shot), the parser often struggles to select the correct set of database constants in the new database, due to the local…  (More)
    • EMNLP 2019
      Jonathan Herzig, Jonathan Berant
      A major hurdle on the road to conversational interfaces is the difficulty in collecting data that maps language utterances to logical forms. One prominent approach for data collection has been to automatically generate pseudo-language paired with logical forms, and paraphrase the pseudo-language to…  (More)
    • EMNLP 2019
      Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Daniel S. Weld
      As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. Recent successful models for this task have used hierarchical models to…  (More)
    • EMNLP 2019
      Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, Omer Levy
      We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span…  (More)
    • EMNLP 2019
      Hila Gonen, Yoav Goldberg
      We focus on the problem of language modeling for code-switched language, in the context of automatic speech recognition (ASR). Language modeling for code-switched language is challenging for (at least) three reasons: (1) lack of available large-scale code-switched data for training; (2) lack of a…  (More)
    • EMNLP 2019
      Matan Ben Noach, Yoav Goldberg
      Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained based on expected label proportions. We…  (More)
    • EMNLP 2019
      Ben Zhou, Daniel Khashabi, Qiang Ning, Dan Roth
      Understanding time is crucial for understanding events expressed in natural language. Because people rarely say the obvious, it is often necessary to have commonsense knowledge about various temporal aspects of events, such as duration, frequency, and temporal order. However, this important problem…  (More)
    • EMNLP 2019
      Mandar Joshi, Omer Levy, Daniel S. Weld, Luke Zettlemoyer
      We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but…  (More)
    • EMNLP 2019
      Sewon Min, Danqi Chen, Hannaneh Hajishirzi, Luke Zettlemoyer
      Many question answering (QA) tasks only provide weak supervision for how the answer should be computed. For example, TriviaQA answers are entities that can be mentioned multiple times in supporting documents, while DROP answers can be computed by deriving many different equations from numbers in…  (More)
    • EMNLP 2019
      Lianhui Qin, Antoine Bosselut, Ari Holtzman, Chandra Bhagavatula, Elizabeth Clark, Yejin Choi
      Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes. Despite being considered a necessary component of AI-complete systems, few resources have been developed for evaluating counterfactual reasoning in…  (More)
    • EMNLP 2019
      Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. Smith
      Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e.g., accuracy) on held-out test data, compared to previous results. In this paper, we demonstrate that test-set performance scores alone are insufficient for drawing accurate…  (More)
    • EMNLP 2019
      Jesse Dodge, Roy Schwartz, Hao Peng, Noah A. Smith
      Neural models for NLP typically use large numbers of parameters to reach state-of-the-art performance, which can lead to excessive memory usage and increased runtime. We present a structure learning method for learning sparse, parameter-efficient NLP models. Our method applies group lasso to…  (More)
    • EMNLP 2019
      Pradeep Dasigi, Nelson F. Liu, Ana Marasovi'c, Noah A. Smith, Matt Gardner
      Machine comprehension of texts longer than a single sentence often requires coreference resolution. However, most current reading comprehension benchmarks do not contain complex coreferential phenomena and hence fail to evaluate the ability of models to resolve coreference. We present a new…  (More)