Foundations of statistical natural language processing
Foundations of statistical natural language processing
Automatic optimization of dialogue management
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
The Knowledge Engineering Review
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Learning more effective dialogue strategies using limited dialogue move features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Incremental Classification with Generalized Eigenvalues
Journal of Classification
User simulation in a stochastic dialog system
Computer Speech and Language
A statistical approach to spoken dialog systems design and evaluation
Speech Communication
Data-driven user simulation for automated evaluation of spoken dialog systems
Computer Speech and Language
User simulations for context-sensitive speech recognition in spoken dialogue systems
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Response-based confidence annotation for spoken dialogue systems
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Sphinx-4: a flexible open source framework for speech recognition
Sphinx-4: a flexible open source framework for speech recognition
The Knowledge Engineering Review
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Computer Speech and Language
Collaborative health care plan support
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
| Hi-index | 0.00 |
A spoken dialogue system (SDS) interacts with its user in a spoken natural language. It interprets user speech input and responds to the user. User speech in a spoken natural language may be ambiguous. A challenge in building an SDS is dealing with ambiguity. Without good abilities for disambiguation, an SDS can hardly have meaningful and smooth dialogues with its user in practical applications. The existing techniques for disambiguation are mainly based on statistical knowledge about language use. In practical situations, such knowledge alone is inadequate. In our research, we develop a new disambiguation technique, which is based on application of knowledge about user activity behavior, in addition to knowledge about language use. The technique is named MUBOD, standing for modeling user behavior online for disambiguation. The core component of MUBOD is an online reinforcement learning algorithm that is used to learn the knowledge and apply the knowledge for disambiguation. In this paper, we describe the technique and its implementation, and present and analyze some initial experimental results.