C4.5: programs for machine learning
C4.5: programs for machine learning
Towards a tool for the Subjective Assessment of Speech System Interfaces (SASSI)
Natural Language Engineering
Recent improvements in the CMU spoken language understanding system
HLT '94 Proceedings of the workshop on Human Language Technology
On the means for clarification in dialogue
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Using machine learning to explore human multimodal clarification strategies
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Error awareness and recovery in conversational spoken language interfaces
Error awareness and recovery in conversational spoken language interfaces
The RavenClaw dialog management framework: Architecture and systems
Computer Speech and Language
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
Predicting concept types in user corrections in dialog
SRSL '09 Proceedings of the 2nd Workshop on Semantic Representation of Spoken Language
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
Hi-index | 0.00 |
This paper presents a progressively challenging series of experiments that investigate clarification subdialogues to resolve the words in noisy transcriptions of user utterances. We focus on user utterances where the user's specific intent requires little additional inference, given sufficient understanding of the form. We learned decision-making strategies for a dialogue manager from run-time features of our spoken dialogue system and from observation of human wizards we had embedded within it. Results show that noisy ASR can be resolved based on predictions from context about what a user might say, and that dialogue management strategies for clarifications of linguistic form benefit from access to features from spoken language understanding.