Speech-based Information Retrieval System with Clarification Dialogue Strategy

Teruhisa Misu, Tatsuya Kawahara

Abstract: This paper addresses a dialogue strategy to clarify and constrain the queries for speech-driven document retrieval systems. In spoken dialogue interfaces, users often make utterances before the query is completely generated in their mind; thus input queries are often vague or fragmental. As a result, usually many items are matched. We propose an efficient dialogue framework, where the system dynamically selects an optimal question based on information gain (IG), which represents reduction of matched items. A set of possible questions is prepared using various knowledge sources. As a bottom-up knowledge source, we extract a list of words that can take a number of objects and potentially causes ambiguity, using a dependency structure analysis of the document texts. This is complemented by top-down knowledge sources of metadata and hand-crafted questions. An experimental evaluation showed that the method significantly improved the success rate of retrieval, and all categories of the prepared questions contributed to the improvement.