Dialogue natural language inference

Sean Welleck, Jason Weston, Arthur Szlam, Kyunghyun Cho

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

Abstract

Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue model, and evaluate the method with human evaluation and with automatic metrics on a suite of evaluation sets designed to measure a dialogue model's consistency.

Original languageEnglish (US)
Title of host publicationACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages3731-3741
Number of pages11
ISBN (Electronic)9781950737482
StatePublished - Jan 1 2020
Event57th Annual Meeting of the Association for Computational Linguistics, ACL 2019 - Florence, Italy
Duration: Jul 28 2019Aug 2 2019

Publication series

NameACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference57th Annual Meeting of the Association for Computational Linguistics, ACL 2019
CountryItaly
CityFlorence
Period7/28/198/2/19

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science(all)
  • Linguistics and Language

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  • Cite this

    Welleck, S., Weston, J., Szlam, A., & Cho, K. (2020). Dialogue natural language inference. In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 3731-3741). (ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference). Association for Computational Linguistics (ACL).