Paper: Discriminative state tracking for spoken dialog systems

ACL ID P13-1046
Title Discriminative state tracking for spoken dialog systems
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013

In spoken dialog systems, statistical state tracking aims to improve robustness to speech recognition errors by tracking a posterior distribution over hidden dialog states. Current approaches based on gener- ative or discriminative models have differ- ent but important shortcomings that limit their accuracy. In this paper we discuss these limitations and introduce a new ap- proach for discriminative state tracking that overcomes them by leveraging the problem structure. An offline evaluation with dialog data collected from real users shows improvements in both state track- ing accuracy and the quality of the pos- terior probabilities. Features that encode speech recognition error patterns are par- ticularly helpful, and training requires rel- atively few dialogs.