@inproceedings{liu-etal-2020-impress,
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",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.131/",
doi = "10.18653/v1/2020.acl-main.131",
pages = "1417--1427",
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{\textasciicircum}2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding. Specifically, P{\textasciicircum}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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T You Impress Me: Dialogue Generation via Mutual Persona Perception
%A Liu, Qian
%A Chen, Yihong
%A Chen, Bei
%A Lou, Jian-Guang
%A Chen, Zixuan
%A Zhou, Bin
%A Zhang, Dongmei
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-impress
%X 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.
%R 10.18653/v1/2020.acl-main.131
%U https://aclanthology.org/2020.acl-main.131/
%U https://doi.org/10.18653/v1/2020.acl-main.131
%P 1417-1427
Markdown (Informal)
[You Impress Me: Dialogue Generation via Mutual Persona Perception](https://aclanthology.org/2020.acl-main.131/) (Liu et al., ACL 2020)
ACL