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.

翻译：一致性是对话模式面临的长期长期问题。在本文件中,我们将对话代理人的一致性作为自然语言推断(NLI)来界定,并创建一个新的自然语言推断数据集,称为 " 对话NLI " 。我们提出一种方法,表明可使用经培训的 " 对话NLI " 模型来改进对话模式的一致性,评价与人文评价和一套评价组合的自动衡量标准的方法,这些评价组合旨在衡量对话模式的一致性。