This paper presents a method for building a personalized open-domain dialogue system to address the $\textit{WWH}$ ($\textit{WHAT}$, $\textit{WHEN}$, and $\textit{HOW}$) problem for natural response generation in a commercial setting, where personalized dialogue responses are heavily interleaved with casual response turns. The proposed approach involves weighted dataset blending, negative persona information augmentation methods, and the design of personalized conversation datasets to address the challenges of $\textit{WWH}$ in personalized, open-domain dialogue systems. Our work effectively balances dialogue fluency and tendency to ground, while also introducing a response-type label to improve the controllability and explainability of the grounded responses. The combination of these methods leads to more fluent conversations, as evidenced by subjective human evaluations as well as objective evaluations.

本文提出了一种为解决商业场景下个性化对话系统中问答匹配(What,When,How)问题的方法，该方法涉及加权数据集合并、负面个人信息增强方法、个性化对话数据集的设计来平衡对话流畅和关联性，同时引入反馈响应类型标签来提高可控性和可解释性。这些方法的结合导致更流畅的对话，这在客观和主观的评估中都得到了证实。

如何设计用户角色感知的会话代理以进行有效对话