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    • WACV 2020
      Moshiko Raboh, Roei Herzig, Gal Chechik, Jonathan Berant, Amir Globerson
      Understanding the semantics of complex visual scenes involves perception of entities and reasoning about their relations. Scene graphs provide a natural representation for these tasks, by assigning labels to both entities (nodes) and relations (edges). However, scene graphs are not commonly used as…  (More)
    • AAAI 2020
      Tushar Khot, Peter Clark, Michal Guerquin, Paul Edward Jansen, Ashish Sabharwal
      Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice…  (More)
    • AAAI 2020
      Kyle Richardson, Hai Na Hu, Lawrence S. Moss, Ashish Sabharwal
      Do state-of-the-art models for language understanding already have, or can they easily learn, abilities such as boolean coordination, quantification, conditionals, comparatives, and monotonicity reasoning (i.e., reasoning about word substitutions in sentential contexts)? While such phenomena are…  (More)
    • SCIL 2020
      Hai Hu, Qi Chen, Kyle Richardson, Atreyee Mukherjee, Lawrence S. Moss, Sandra Kübler
      We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus. In contrast to existing logic-based approaches, our system is intentionally designed to be as lightweight as possible, and operates using…  (More)
    • NeurIPS 2019
      Mitchell Wortsman, Ali Farhadi, Mohammad Rastegari
      The success of neural networks has driven a shift in focus from feature engineering to architecture engineering. However, successful networks today are constructed using a small and manually defined set of building blocks. Even in methods of neural architecture search (NAS) the network connectivity…  (More)
    • EMNLP 2019
      Tushar Khot, Ashish Sabharwal, Peter Clark
      Multi-hop textual question answering requires combining information from multiple sentences. We focus on a natural setting where, unlike typical reading comprehension, only partial information is provided with each question. The model must retrieve and use additional knowledge to correctly answer…  (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
      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
      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
      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
      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
      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)