Word Level Confidence Annotation Using Combinations of Features
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This paper describes the development of a word-level confidence metric suitable for use in a dialog system. Two aspects of the problems are investigated: the identification of useful features and the selection of an effective classifier. We find that two parse-level features, Parsing-Mode and SlotBackoff-Mode, provide annotation accuracy comparable to that observed for decoder-level features. However, both decoderlevel and parse-level features independently contribute to confidence annotation accuracy. In comparing different classification techniques, we found that Support Vector Machines (SVMs) appear to provide the best accuracy. Overall we achieve 39.7% reduction in annotation uncertainty for a binary confidence decision in a travel-planning domain.