You Impress Me: Dialogue Generation via Mutual Persona Perception
Qian Liu, Yihong Chen, Bei Chen, Jian-Guang Lou, Zixuan Chen, Bin Zhou, Dongmei Zhang
Dialogue and Interactive Systems Long Paper
Session 2B: Jul 6
(09:00-10:00 GMT)
Session 3A: Jul 6
(12:00-13:00 GMT)
Abstract:
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.
You can open the
pre-recorded video
in a separate window.
NOTE: The SlidesLive video may display a random order of the authors.
The correct author list is shown at the top of this webpage.
Similar Papers
Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation
Haoyu Song, Yan Wang, Wei-Nan Zhang, Xiaojiang Liu, Ting Liu,

Guiding Variational Response Generator to Exploit Persona
Bowen Wu, Mengyuan Li, Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun Wang,

Image-Chat: Engaging Grounded Conversations
Kurt Shuster, Samuel Humeau, Antoine Bordes, Jason Weston,
