1. Paper title

You Impress Me: Dialogue Generation via Mutual Persona Perception

2. link

https://www.aclweb.org/anthology/2020.acl-main.131.pdf

3. 摘要

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-theart baselines across both automatic metrics and human evaluations.

Engagingness: 参与度
mimicking :模仿
Interlocutors: 对话者
cognitive science: 认可科学

4. 要解决什么问题

模型对对话内容的认知。

5. 作者的主要贡献

P 2 BOT：一个基于transmitter-receiver的框架。
以理解内容为目的的模型。
融合了共同的角色意识，以提高个性化对话生成的质量。

6. 得到了什么结果

PERSONA-CHAT，自动评价指标和人工评价指标上都优于SOTA。

7. 关键字

个性化