In this work, we study the effectiveness of state-of-the-art, sophisticated supervised learning algorithms for dialogue act modeling across a comprehensive set of different spoken and written conversations including: emails, forums, meetings, and phone conversations. To this aim, we compare the results of SVM-multiclass and two structured predictors namely SVMhmm and CRF algorithms. Extensive empirical results, across different conversational modalities, demonstrate the effectiveness of our SVM-hmm model for dialogue act recognition in conversations.

@inproceedings{tavafi-et-al-sigdial-13, abstract = {In this work, we study the effectiveness of state-of-the-art, sophisticated supervised learning algorithms for dialogue act modeling across a comprehensive set of different spoken and written conversations including: emails, forums, meetings, and phone conversations. To this aim, we compare the results of SVM-multiclass and two structured predictors namely SVMhmm and CRF algorithms. Extensive empirical results, across different conversational modalities, demonstrate the effectiveness of our SVM-hmm model for dialogue act recognition in conversations.}, address = {Metz, France}, author = {Tavafi, Maryam and Mehdad, Yashar and Joty, Shafiq and Carenini, Giuseppe and Ng, Raymond}, booktitle = {Proceedings of the Special Interest Group on Discourse and Dialogue Conference}, month = {August}, pages = {117--121}, publisher = {Association for Computational Linguistics}, series = {SIGDIAL'13}, title = {Dialogue Act Recognition in Synchronous and Asynchronous Conversations}, url = {http://www.aclweb.org/anthology/W13-4017}, year = {2013} }