Dialogue act recognition in synchronous and asynchronous conversations

Maryam Tavafi, Yashar Mehdad, Shafiq Rayhan Joty, Giuseppe Carenini, Raymond Ng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationSIGDIAL 2013 - 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages117-121
Number of pages5
ISBN (Electronic)9781937284954
Publication statusPublished - 2013
Event14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2013 - Metz, France
Duration: 22 Aug 201324 Aug 2013

Other

Other14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2013
CountryFrance
CityMetz
Period22/8/1324/8/13

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ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Modelling and Simulation
  • Human-Computer Interaction

Cite this

Tavafi, M., Mehdad, Y., Rayhan Joty, S., Carenini, G., & Ng, R. (2013). Dialogue act recognition in synchronous and asynchronous conversations. In SIGDIAL 2013 - 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference (pp. 117-121). Association for Computational Linguistics (ACL).