Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Spoken dialogue management using probabilistic reasoning
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
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
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Computer Speech and Language
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Computer Speech and Language
Effective handling of dialogue state in the hidden information state POMDP-based dialogue manager
ACM Transactions on Speech and Language Processing (TSLP)
ACM Transactions on Speech and Language Processing (TSLP)
An unsupervised approach to user simulation: toward self-improving dialog systems
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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This paper proposes the use of unsupervised approaches to improve components of partition-based belief tracking systems. The proposed method adopts a dynamic Bayesian network to learn the user action model directly from a machine-transcribed dialog corpus. It also addresses confidence score calibration to improve the observation model in a unsupervised manner using dialog-level grounding information. To verify the effectiveness of the proposed method, we applied it to the Let's Go domain (Raux et al., 2005). Overall system performance for several comparative models were measured. The results show that the proposed method can learn an effective user action model without human intervention. In addition, the calibrated confidence score was verified by demonstrating the positive influence on the user action model learning process and on overall system performance.