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Proceedings of the 6th international conference on Intelligent user interfaces
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HLT '94 Proceedings of the workshop on Human Language Technology
Characterizing and Predicting Corrections in Spoken Dialogue Systems
Computational Linguistics
Human-Computer Interaction (3rd Edition)
Human-Computer Interaction (3rd Edition)
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COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
The RavenClaw dialog management framework: Architecture and systems
Computer Speech and Language
Agent Mining: The Synergy of Agents and Data Mining
IEEE Intelligent Systems
MICA: a probabilistic dependency parser based on tree insertion grammars application note
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Optimizing endpointing thresholds using dialogue features in a spoken dialogue system
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Predicting concept types in user corrections in dialog
SRSL '09 Proceedings of the 2nd Workshop on Semantic Representation of Spoken Language
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SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Learning about voice search for spoken dialogue systems
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Learning to balance grounding rationales for dialogue systems
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Feature selection for error detection and recovery in spoken dialogue systems
Feature selection for error detection and recovery in spoken dialogue systems
Semantic specificity in spoken dialogue requests
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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Next-generation autonomous agents will be expected to converse with people to achieve their mutual goals. Human-machine dialogue, however, is challenged by noisy acoustic data, and by people's preference for more natural interaction. This paper describes an ambitious project that embeds human subjects in a spoken dialogue system. It collects a rich and novel data set, including spoken dialogue, human behavior, and system features. During data collection, subjects were restricted to the same databases, action choices, and noisy automated speech recognition output as a spoken dialogue system. This paper mines that data to learn how people manage the problems that arise during dialogue under such restrictions. Two different approaches to successful, goal-directed dialogue are identified this way, from which supervised learning can predict appropriate dialogue choices. The resultant models can then be incorporated into an autonomous agent that seeks to assist its user.