Generating More Specific Questions for Acquiring Attributes of Unknown Concepts from Users

Our aim is to acquire the attributes of concepts denoted by unknown words from users during dialogues. A word unknown to spoken dialogue systems can appear in user utterances, and systems should be capable of acquiring information on it from the conversation partner as a kind of selflearning process. As a first step, we propose a method for generating more specific questions than simple wh-questions to acquire the attributes, as such questions can narrow down the variation of the following user response and accordingly avoid possible speech recognition errors. Specifically, we obtain an appropriately distributed confidence measure (CM) on the attributes to generate more specific questions. Two basic CMs are defined using (1) character and word distributions in the target database and (2) frequency of occurrence of restaurant attributes on Web pages. These are integrated to complement each other and used as the final CM. We evaluated distributions of the CMs by average errors from the reference. Results showed that the integrated CM outperformed the two basic CMs.