Bayes risk-based dialogue management for document retrieval system with speech interface

We propose an efficient technique of dialogue management for an information navigation system based on a document knowledge base. The system can use ASR N-best hypotheses and contextual information to perform robustly for fragmental speech input and erroneous output of automatic speech recognition (ASR). It also has several choices in generating responses or confirmations. We formulate the optimization of these choices based on a Bayes risk criterion, which is defined based on a reward for correct information presentation and a penalty for redundant turns. The parameters for the dialogue management we propose can be adaptively tuned by online learning. We evaluated this strategy with our spoken dialogue system called “Dialogue Navigator for Kyoto City”, which generates responses based on the document retrieval and also has question–answering capability. The effectiveness of the proposed framework was demonstrated by the increased success rate of dialogue and the reduced number of turns for information access through an experiment with a large number of utterances by real users.