Dialogue Natural Language Inference

Sean Welleck, Jason Weston, Arthur Szlam, Kyunghyun Cho


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.
Anthology ID:
P19-1363
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3731–3741
URL:
https://www.aclweb.org/anthology/P19-1363
DOI:
10.18653/v1/P19-1363
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PDF:
https://www.aclweb.org/anthology/P19-1363.pdf