Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Combining lexical, syntactic and prosodic cues for improved online dialog act tagging
Computer Speech and Language
Cascaded model adaptation for dialog act segmentation and tagging
Computer Speech and Language
Modelling and detecting decisions in multi-party dialogue
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
It's not you, it's me: detecting flirting and its misperception in speed-dates
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Semi-supervised speech act recognition in emails and forums
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Domain adaptation with unlabeled data for dialog act tagging
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Speaker-adaptive multimodal prediction model for listener responses
Proceedings of the 15th ACM on International conference on multimodal interaction
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In this work we study the effectiveness of speaker adaptation for dialogue act recognition in multiparty meetings. First, we analyze idiosyncracy in dialogue verbal acts by qualitatively studying the differences and conflicts among speakers and by quantitively comparing speaker-specific models. Based on these observations, we propose a new approach for dialogue act recognition based on reweighted domain adaptation which effectively balance the influence of speaker specific and other speakers' data. Our experiments on a real-world meeting dataset show that with even only 200 speaker-specific annotated dialogue acts, the performances on dialogue act recognition are significantly improved when compared to several baseline algorithms. To our knowledge, this work is the first to tackle this promising research direction of speaker adaptation for dialogue act recogntion.