Total: 1
Adequate confirmation is indispensable in spoken dialog systems to eliminate misunderstandings caused by speech recognition errors. Spoken language also inherently includes redundant expressions such as disfluency and out-of-domain phrases, which do not contribute to task achievement. It is easy to define a set of keywords to be confirmed for conventional database query tasks, but not straightforward in general document retrieval tasks. In this paper, we propose two statistical measures for identifying portions to be confirmed. A relevance score (RS) represents matching degree with the document set. A significance score (SS) detects portions that consequently affect the retrieval results. With these measures, the system can generate confirmation prior to and posterior to the retrieval, respectively. The strategy is implemented and evaluated with retrieval from software support knowledge base of 40K entries. It is shown that the proposed strategy using the two measures is more efficient than using the conventional confidence measure.