Generating Persona Consistent Dialogues by Exploiting Natural Language Inference

Understand

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.