Using Natural Language Processing and discourse Features to Identify Understanding Errors
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Characterizing and recognizing spoken corrections in human-computer dialogue
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Characterizing and Predicting Corrections in Spoken Dialogue Systems
Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Extending boosting for large scale spoken language understanding
Machine Learning
Introduction to Information Retrieval
Introduction to Information Retrieval
Automating Model Building in c-rater
TextInfer '09 Proceedings of the 2009 Workshop on Applied Textual Inference
Concept form adaptation in human-computer dialog
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
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Data mining to support human-machine dialogue for autonomous agents
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
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Most dialog systems explicitly confirm user-provided task-relevant concepts. User responses to these system confirmations (e.g. corrections, topic changes) may be misrecognized because they contain unrequested task-related concepts. In this paper, we propose a concept-specific language model adaptation strategy where the language model (LM) is adapted to the concept type(s) actually present in the user's post-confirmation utterance. We evaluate concept type classification and LM adaptation for post-confirmation utterances in the Let's Go! dialog system. We achieve 93% accuracy on concept type classification using acoustic, lexical and dialog history features. We also show that the use of concept type classification for LM adaptation can lead to improvements in speech recognition performance.