An affect-enriched dialogue act classification model for task-oriented dialogue

  • Authors:

  • Kristy Elizabeth Boyer;Joseph F. Grafsgaard;Eun Young Ha;Robert Phillips;James C. Lester

  • Affiliations:

  • North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;Dual Affiliation with Applied Research Associates, Inc., Raleigh, NC;North Carolina State University, Raleigh, NC

  • Venue:
  • HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
  • Year:
  • 2011

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Abstract

Dialogue act classification is a central challenge for dialogue systems. Although the importance of emotion in human dialogue is widely recognized, most dialogue act classification models make limited or no use of affective channels in dialogue act classification. This paper presents a novel affect-enriched dialogue act classifier for task-oriented dialogue that models facial expressions of users, in particular, facial expressions related to confusion. The findings indicate that the affect-enriched classifiers perform significantly better for distinguishing user requests for feedback and grounding dialogue acts within textual dialogue. The results point to ways in which dialogue systems can effectively leverage affective channels to improve dialogue act classification.