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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)
    • arXiv 2019
      Alon Talmor, Yanai Elazar, Yoav Goldberg, Jonathan Berant
      Recent success of pre-trained language models (LMs) has spurred widespread interest in the language capabilities that they possess. However, efforts to understand whether LM representations are useful for symbolic reasoning tasks have been limited and scattered. In this work, we propose eight…  (More)
    • arXiv 2019
      Kyle Richardson, Ashish Sabharwal
      Open-domain question answering (QA) is known to involve several underlying knowledge and reasoning challenges, but are models actually learning such knowledge when trained on benchmark tasks? To investigate this, we introduce several new challenge tasks that probe whether state-of-theart QA models…  (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)
    • arXiv 2019
      Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon
      Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics. However, this problem is also #P-complete, which leaves little hope for finding an exact solution that can be computed efficiently. While the…  (More)
    • arXiv 2019
      Erfan Sadeqi Azer, Daniel Khashabi, Ashish Sabharwal, Dan Roth
      Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known issues. While alternative proposals have been well-debated and adopted in other fields, they remain rarely…  (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 • MRQA Workshop 2019
      Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, Sewon Min
      Machine reading comprehension, the task of evaluating a machine’s ability to comprehend a passage of text, has seen a surge in popularity in recent years. There are many datasets that are targeted at reading comprehension, and many systems that perform as well as humans on some of these datasets…  (More)
    • EMNLP • MRQA Workshop 2019
      Dheeru Dua, Ananth Gottumukkala, Alon Talmor, Sameer Singh, Matt Gardner
      Reading comprehension is one of the crucial tasks for furthering research in natural language understanding. A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple paraphrase matching and entity typing to…  (More)
    • EMNLP • MRQA Workshop 2019
      Anthony Chen, Gabriel Stanovsky, Sameer Singh, Matt Gardner
      As the complexity of question answering (QA) datasets evolve, moving away from restricted formats like span extraction and multiple-choice (MC) to free-form answer generation, it is imperative to understand how well current metrics perform in evaluating QA. This is especially important as existing…  (More)
    • EMNLP • MRQA Workshop 2019
      Kevin Lin, Oyvind Tafjord, Peter Clark, Matt Gardner
      A key component of successfully reading a passage of text is the ability to apply knowledge gained from the passage to a new situation. In order to facilitate progress on this kind of reading, we present ROPES, a challenging benchmark for reading comprehension targeting Reasoning Over Paragraph…  (More)
    • EMNLP 2019
      Eric Wallace, Shi Feng, Nikhil Kandpal, Matthew Gardner, Sameer Singh
      dversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset. We propose a…  (More)
    • EMNLP 2019
      Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, Matt Gardner
      The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other tokens---they embed them as distributed vectors. Is this enough to capture numeracy? We begin by investigating the…  (More)
    • EMNLP 2019
      Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matthew Gardner, Sameer Singh
      Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior. Model interpretation methods ameliorate this opacity by providing explanations for specific model predictions. Unfortunately, existing…  (More)
    • EMNLP • W-NUT 2019
      Gabriel Stanovsky, Ronen Tamari
      Distinguishing between singular and plural "you" in English is a challenging task which has potential for downstream applications, such as machine translation or coreference resolution. While formal written English does not distinguish between these cases, other languages (such as Spanish), as well…  (More)
    • CoNLL 2019
      Omri Koshorek, Gabriel Stanovsky, Yichu Zhou, Vivek Srikumar and Jonathan Berant
      One of the goals of natural language understanding is to develop models that map sentences into meaning representations. However, training such models requires expensive annotation of complex structures, which hinders their adoption. Learning to actively-learn (LTAL) is a recent paradigm for…  (More)
    • CoNLL 2019
      Phoebe Mulcaire, Jungo Kasai, Noah A. Smith
      Despite advances in dependency parsing, languages with small treebanks still present challenges. We assess recent approaches to multilingual contextual word representations (CWRs), and compare them for crosslingual transfer from a language with a large treebank to a language with a small or…  (More)