Modeling the user in natural language systems
Computational Linguistics - Special issue on user modeling
Tailoring object descriptions to a user's level of expertise
Computational Linguistics - Special issue on user modeling
C4.5: programs for machine learning
C4.5: programs for machine learning
Predicting and Adapting to Poor Speech Recognition in a Spoken Dialogue System
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
MIMIC: an adaptive mixed initiative spoken dialogue system for information queries
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A model for generating better explanations
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
The LIMSI ARISE system for train travel information
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
DialogXML: extending VoiceXML for dynamic dialog management
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Selection of Optimum Vocabulary and Dialog Strategy for Noise-Robust Spoken Dialog Systems
IEICE - Transactions on Information and Systems
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We have developed a telephone-based cooperative natural language dialogue system. Since natural language involves very various expressions, a large number of VoiceXML scripts need to be prepared to handle all possible input patterns. Thus, flexible dialogue management for various user utterances is realized by generating VoiceXML scripts dynamically. Moreover, we address the issue of appropriate user modeling to generate cooperative responses to users. Specifically, three dimensions of user models are set up: the skill level to the system, the knowledge level on the target domain and the degree of hastiness. The models are automatically derived by decision tree learning using real dialogue data collected by the system. Experimental evaluation showed that the cooperative responses adapted to individual users served as good guides for novices without increasing the duration of dialogue for skilled users.