Using Natural Language Processing and discourse Features to Identify Understanding Errors
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Conversation as Action Under Uncertainty
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
Reacting to agreement and error in spoken dialogue systems using degrees of groundedness
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Building conversational agents with Basilica
NAACL-Demonstrations '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Demonstration Session
Degrees of grounding based on evidence of understanding
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Improving a virtual human using a model of degrees of grounding
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Hybrid approach to robust dialog management using agenda and dialog examples
Computer Speech and Language
Spoken language understanding via supervised learning and linguistically motivated features
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Learning to balance grounding rationales for dialogue systems
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
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We describe the error handling architectture underlying the RavenClaw dialog management framework. The architecture provides a robust basis for current and future research in error detection and recovery. Several objectives were pursued in its development: task-independence, ease-of-use, adaptability and scalability. We describe the key aspects of architectural design which confer these properties, and discuss the deployment of this architectture in a number of spoken dialog systems spanning several domains and interaction types. Finally, we outline current research projects supported by this architecture.