Speech Communication - Special issue on interactive voice technology for telecommunication applications
The Knowledge Engineering Review
Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Dialogue management in the Mercury flight reservation system
ConversationalSys '00 Proceedings of the ANLP-NAACL 2000 Workshop on Conversational Systems
NJFun: a reinforcement learning spoken dialogue system
ConversationalSys '00 Proceedings of the ANLP-NAACL 2000 Workshop on Conversational Systems
Conquest: an open-source dialog system for conferences
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
A probabilistic framework for dialog simulation and optimal strategy learning
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, we present a statistical approach for the automatic generation of dialogs by means of a user simulator. This technique can be used to generate dialogs with reduced effort, facilitating the evaluation and improvement of spoken dialog systems. In our approach for user simulation, the user answer is selected taking into account the history of the dialog and the last system turn, as well as the objective(s) set for the dialog. The user model is automatically learned from a training corpus that is labeled in terms of dialog acts. This methodology has been evaluated within the framework of the DIHANA project, whose goal is the design and development of a dialog system to access a railway information system using spontaneous speech in Spanish.