  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.
