Effective dialogue involves grounding, the process of establishing mutual
knowledge that is essential for communication between people. Modern dialogue
systems are not explicitly trained to build common ground, and therefore
overlook this important aspect of communication. Improvisational theater
(improv) intrinsically contains a high proportion of dialogue focused on
building common ground, and makes use of the yes-and principle, a strong
grounding speech act, to establish coherence and an actionable objective
reality. We collect a corpus of more than 26,000 yes-and turns, transcribing
them from improv dialogues and extracting them from larger, but more sparsely
populated movie script dialogue corpora, via a bootstrapped classifier. We
fine-tune chit-chat dialogue systems with our corpus to encourage more
grounded, relevant conversation and confirm these findings with human
evaluations.

本研究探讨了在对话中建立共同知识的重要性，并提出了一种使用即兴剧场中的 yes-and 原则构建共同语境的方法，并且使用 fine-tune 后的聊天机器人与人类评估确认了这一结论。

用即兴对话澄清谈话

Grounding Conversations with Improvised Dialogues

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.

本文提出了基于 P^2 框架的机器人 P^2 Bot，该框架旨在显式地建立对话者间的理解模型。实验结果表明该框架在大型公共数据集 Persona-Chat 上取得了显著效果提升。