Corpus-based discourse understanding in spoken dialogue systems
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Computer Speech and Language
Optimising information presentation for spoken dialogue systems
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Natural language generation as planning under uncertainty for spoken dialogue systems
Empirical methods in natural language generation
An empirical evaluation of a statistical dialog system in public use
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
What if everyone could do it?: a framework for easier spoken dialog system design
Proceedings of the 5th ACM SIGCHI symposium on Engineering interactive computing systems
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In spoken dialogue systems, Partially Observable Markov Decision Processes (POMDPs) provide a formal framework for making dialogue management decisions under uncertainty, but efficiency and interpretability considerations mean that most current statistical dialogue managers are only MDPs. These MDP systems encode uncertainty explicitly in a single state representation. We formalise such MDP states in terms of distributions over POMDP states, and propose a new dialogue system architecture (Mixture Model POMDPs) which uses mixtures of these distributions to efficiently represent uncertainty. We also provide initial evaluation results (with real users) for this architecture.