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arXiv · 1811.00671

Dialogue Natural Language Inference

Sean Welleck·Jason Weston·Arthur Szlam·Kyunghyun Cho
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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.

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Sean Welleck, Jason Weston, Arthur Szlam, Kyunghyun Cho. 2018-11-01. Dialogue Natural Language Inference. https://arxiv.org/abs/1811.00671

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