You Impress Me: Dialogue Generation via Mutual Persona Perception

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Introduction

Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors. The research in cognitive science, instead, suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P^2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding. Specifically, P^2 Bot incorporates mutual persona perception to enhance the quality of personalized dialogue generation. Experiments on a large public dataset, Persona-Chat, demonstrate the effectiveness of our approach, with a considerable boost over the state-of-the-art baselines across both automatic metrics and human evaluations.

Cite

@inproceedings{liu-etal-2020-personachat,
    title = "You Impress Me: Dialogue Generation via Mutual Persona Perception",
    author = "Liu, Qian and Chen, Yihong and Chen, Bei and Lou, Jian-Guang and Chen, Zixuan and Zhou, Bin and Zhang, Dongmei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    year = "2020",
    publisher = "Association for Computational Linguistics"
}