论文标题
在电影推荐对话中建模和利用用户的内部状态
Modeling and Utilizing User's Internal State in Movie Recommendation Dialogue
论文作者
论文摘要
智能对话系统被预期是人类和机器之间的新接口。这样的智能对话系统应在对话中估算用户的内部状态（UIS），并根据估计结果适当更改其响应。在本文中，我们在对话中对UI进行建模，以电影推荐对话为示例，并构建一个根据UIS更改其响应的对话系统。根据对话数据分析，我们将UIS模拟为三个要素：知识，兴趣和参与。我们使用建模UIS的注释来训练UIS估计器的对话语料库。估计器达到了较高的估计精度。我们还设计了响应更改规则，该规则会根据每个UI都更改系统的响应。我们证实，使用UIS估计器的结果进行了响应变化，可以在对话评估和话语评估中改善系统话语的自然性。
Intelligent dialogue systems are expected as a new interface between humans and machines. Such an intelligent dialogue system should estimate the user's internal state (UIS) in dialogues and change its response appropriately according to the estimation result. In this paper, we model the UIS in dialogues, taking movie recommendation dialogues as examples, and construct a dialogue system that changes its response based on the UIS. Based on the dialogue data analysis, we model the UIS as three elements: knowledge, interest, and engagement. We train the UIS estimators on a dialogue corpus with the modeled UIS's annotations. The estimators achieved high estimation accuracy. We also design response change rules that change the system's responses according to each UIS. We confirmed that response changes using the result of the UIS estimators improved the system utterances' naturalness in both dialogue-wise evaluation and utterance-wise evaluation.
