Instance-Based Learning Algorithms
Machine Learning
IGTree: Using Trees for Compression and Classification in Lazy LearningAlgorithms
Artificial Intelligence Review - Special issue on lazy learning
Speech Communication - Special issue on interactive voice technology for telecommunication applications (IVITA '96)
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Information Retrieval
Using Natural Language Processing and discourse Features to Identify Understanding Errors
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Predicting and Adapting to Poor Speech Recognition in a Spoken Dialogue System
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Automatic detection of poor speech recognition at the dialogue level
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Correction grammars for error handling in a speech dialog system
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
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We address the issue of on-line detection of communication problems in spoken dialogue systems. The usefulness is investigated of the sequence of system question types and the word graphs corresponding to the respective user utterances. By applying both rule-induction and memory-based learning techniques to data obtained with a Dutch train time-table information system, the current paper demonstrates that the aforementioned features indeed lead to a method for problem detection that performs significantly above baseline. The results are interesting from a dialogue perspective since they employ features that are present in the majority of spoken dialogue systems and can be obtained with little or no computational overhead. The results are interesting from a machine learning perspective, since they show that the rule-based method performs significantly better than the memory-based method, because the former is better capable of representing interactions between features.