Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Developing a flexible spoken dialog system using simulation
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Testing the performance of spoken dialogue systems by means of an artificially simulated user
Artificial Intelligence Review
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
Data-driven user simulation for automated evaluation of spoken dialog systems
Computer Speech and Language
Using automatically transcribed dialogs to learn user models in a spoken dialog system
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Agenda-based user simulation for bootstrapping a POMDP dialogue system
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Computer Speech and Language
Exploiting machine-transcribed dialog corpus to improve multiple dialog states tracking methods
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Modeling spoken dialog systems under the interactive pattern recognition framework
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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This paper proposes an unsupervised approach to user simulation in order to automatically furnish updates and assessments of a deployed spoken dialog system. The proposed method adopts a dynamic Bayesian network to infer the unobservable true user action from which the parameters of other components are naturally derived. To verify the quality of the simulation, the proposed method was applied to the Let's Go domain (Raux et al., 2005) and a set of measures was used to analyze the simulated data at several levels. The results showed a very close correspondence between the real and simulated data, implying that it is possible to create a realistic user simulator that does not necessitate human intervention.