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UBC Theses and Dissertations

Dialogue act recognition in synchronous and asynchronous conversations Maryam, Tavafi


This thesis presents a domain-independent approach for the task of dialogue act modeling across a comprehensive set of different spoken and written conversations including: emails, forums, meetings, and phone conversations. We begin by investigating the performance of unsupervised methods for the task of dialogue act recognition. The low performance of these techniques gives us a motivation to tackle this problem in supervised and semi-supervised manners. To this aim, we propose a domain-independent feature set for the task of dialogue act modeling on different spoken and written conversations. Then, we compare the results of SVM-multiclass and two structured predictors namely SVM-hmm and CRF algorithms for supervised dialogue act modeling. We then provide an in-depth analysis about the effectiveness of proposed domain-independent dialogue act modeling approaches in different written and spoken conversations. Extensive empirical results, across different conversational modalities, demonstrate the effectiveness of our SVM-hmm model for dialogue act recognition in conversations. Furthermore, we use the SVM-hmm algorithm to investigate the effectiveness of using unlabeled data in a semi-supervised dialogue act recognition framework.

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Attribution-NonCommercial-NoDerivatives 4.0 International