  Consistency is one of the major challenges faced by dialogue agents. A
human-like dialogue agent should not only respond naturally, but also maintain
a consistent persona. In this paper, we exploit the advantages of natural
language inference (NLI) technique to address the issue of generating persona
consistent dialogues. Different from existing work that re-ranks the retrieved
responses through an NLI model, we cast the task as a reinforcement learning
problem and propose to exploit the NLI signals from response-persona pairs as
rewards for the process of dialogue generation. Specifically, our generator
employs an attention-based encoder-decoder to generate persona-based responses.
Our evaluator consists of two components: an adversarially trained naturalness
module and an NLI based consistency module. Moreover, we use another
well-performed NLI model in the evaluation of persona-consistency. Experimental
results on both human and automatic metrics, including the model-based
consistency evaluation, demonstrate that the proposed approach outperforms
strong generative baselines, especially in the persona-consistency of generated
responses.
