Dialogue Natural Language Inference

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.

References in corpus (2)

• Transforming Question Answering Datasets Into Natural Language Inference Datasets
• Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering

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• AR-LSAT: Investigating Analytical Reasoning of Text
• Natural Language Inference in Context -- Investigating Contextual Reasoning over Long Texts