Tell me why: Using question answering as distant supervision for answer justification

Rebecca Sharp, Mihai Surdeanu, Peter Jansen, Marco A. Valenzuela-Escárcega, Peter Clark, Michael Hammond

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

For many applications of question answering (QA), being able to explain why a given model chose an answer is critical. However, the lack of labeled data for answer justifications makes learning this difficult and expensive. Here we propose an approach that uses answer ranking as distant supervision for learning how to select informative justifications, where justifications serve as inferential connections between the question and the correct answer while often containing little lexical overlap with either. We propose a neural network architecture for QA that reranks answer justifications as an intermediate (and human-interpretable) step in answer selection. Our approach is informed by a set of features designed to combine both learned representations and explicit features to capture the connection between questions, answers, and answer justifications. We show that with this end-to-end approach we are able to significantly improve upon a strong IR baseline in both justification ranking (+9% rated highly relevant) and answer selection (+6% P@1).

Original languageEnglish (US)
Title of host publicationCoNLL 2017 - 21st Conference on Computational Natural Language Learning, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages69-79
Number of pages11
ISBN (Electronic)9781945626548
DOIs
StatePublished - 2017
Event21st Conference on Computational Natural Language Learning, CoNLL 2017 - Vancouver, Canada
Duration: Aug 3 2017Aug 4 2017

Publication series

NameCoNLL 2017 - 21st Conference on Computational Natural Language Learning, Proceedings

Conference

Conference21st Conference on Computational Natural Language Learning, CoNLL 2017
Country/TerritoryCanada
CityVancouver
Period8/3/178/4/17

ASJC Scopus subject areas

  • Linguistics and Language
  • Artificial Intelligence
  • Human-Computer Interaction

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