Speech-based information retrieval system with clarification dialogue strategy

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 handcrafted 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.