Generating Persona Consistent Dialogues by Exploiting Natural Language Inference


  • Haoyu Song Harbin Institute of Technology
  • Wei-Nan Zhang Harbin Institute of Technology
  • Jingwen Hu Harbin Institute of Technology
  • Ting Liu Harbin Institute of Technology



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.




How to Cite

Song, H., Zhang, W.-N., Hu, J., & Liu, T. (2020). Generating Persona Consistent Dialogues by Exploiting Natural Language Inference. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8878-8885.



AAAI Technical Track: Natural Language Processing