Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
What can I say?: evaluating a spoken language interface to Email
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
International Journal of Human-Computer Studies
Empirically evaluating an adaptable spoken dialogue system
UM '99 Proceedings of the seventh international conference on User modeling
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Evaluating response strategies in a Web-based spoken dialogue agent
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Learning optimal dialogue strategies: a case study of a spoken dialogue agent for email
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Characterizing and recognizing spoken corrections in human-computer dialogue
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
HLT '91 Proceedings of the workshop on Speech and Natural Language
Decoding optimal state sequence with smooth state likelihoods
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Natural Language Processing and User Modeling: Synergies and Limitations
User Modeling and User-Adapted Interaction
Designing and Evaluating an Adaptive Spoken Dialogue System
User Modeling and User-Adapted Interaction
Towards developing general models of usability with PARADISE
Natural Language Engineering
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Predicting automatic speech recognition performance using prosodic cues
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
ACM Transactions on Computer-Human Interaction (TOCHI)
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Identifying user corrections automatically in spoken dialogue systems
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Semantic coherence scoring using an ontology
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Characterizing and Predicting Corrections in Spoken Dialogue Systems
Computational Linguistics
Towards conversational QA: automatic identification of problematic situations and user intent
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
A Model of Temporally Changing User Behaviors in a Deployed Spoken Dialogue System
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Journal of Artificial Intelligence Research
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
Automatically training a problematic dialogue predictor for a spoken dialogue system
Journal of Artificial Intelligence Research
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Contextual coherence in natural language processing
CONTEXT'03 Proceedings of the 4th international and interdisciplinary conference on Modeling and using context
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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The dialogue strategies used by a spoken dialogue system strongly influence performance and user satisfaction. An ideal system would not use a single fixed strategy, but would adapt to the circumstances at hand. To do so, a system must be able to identify dialogue properties that suggest adaptation. This paper focuses on identifying situations where the speech recognizer is performing poorly. We adopt a machine learning approach to learn rules from a dialogue corpus for identifying these situations. Our results show a significant improvement over the baseline and illustrate that both lower-level acoustic features and higher-level dialogue features can affect the performance of the learning algorithm.