Viewing 4 papers from 2020
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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)