Constructing Accurate Beliefs in Spoken Dialog Systems
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We propose a novel approach for constructing more accurate beliefs over concept values in spoken dialog systems by integrating information across multiple turns in the conversation. In particular, we focus our attention on updating the confidence score of the top hypothesis for a concept, in light of subsequent user responses to system confirmation actions. Our data-driven approach bridges previous work in confidence annotation and correction detection, providing a unified framework for belief updating. The approach significantly outperforms heuristic rules currently used in most spoken dialog systems.