论文标题
你给我留下深刻的印象：通过共同的角色感知产生对话
You Impress Me: Dialogue Generation via Mutual Persona Perception
论文作者
论文摘要
尽管不断努力提高chit-chat对话系统的吸引力和一致性，但当前的大多数工作只是着重于模仿人类般的反应，使对话者之间建模理解的方面进行了研究。相反，认知科学的研究表明，理解是高质量聊天对话的重要信号。在此激励的情况下，我们提出了P^2 Bot，这是一种基于发射机接收器的框架，目的是明确建模理解。具体而言，P^2 Bot结合了共同的角色感知，以提高个性化对话的产生质量。在大型公共数据集（Persona-Chat）上进行的实验证明了我们方法的有效性，并且在自动指标和人类评估中的最先进基线比最先进的基线相当大。
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.
