An Affect-Enriched Dialogue Act Classification Model for Task-Oriented Dialogue

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1190–1199, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics An Affect-Enriched Dialogue Act Classification Model for Task-Oriented Dialogue Kristy Elizabeth Boyer Joseph F. Grafsgaard Eun Young Ha Robert Phillips* James C. Lester Department of Computer Science North Carolina State University Raleigh, NC, USA * Dual Affiliation with Applied Research Associates, Inc. Raleigh, NC, USA {keboyer, jfgrafsg, eha, rphilli, lester}@ncsu.edu Abstract Dialogue act classification is a central chal- lenge for dialogue systems. Although the im- portance of emotion in human dialogue is widely recognized, most dialogue act classifi- cation models make limited or no use of affec- tive channels in dialogue act classification. This paper presents a novel affect-enriched dialogue act classifier for task-oriented dia- logue that models facial expressions of users, in particular, facial expressions related to con- fusion. The findings indicate that the affect- enriched classifiers perform significantly bet- ter for distinguishing user requests for feed- back and grounding dialogue acts within textual dialogue. The results point to ways in which dialogue systems can effectively lever- age affective channels to improve dialogue act classification. 1 Introduction Dialogue systems aim to engage users in rich, adaptive natural language conversation. For these systems, understanding the role of a user’s utter- ance in the broader context of the dialogue is a key challenge (Sridhar, Bangalore, & Narayanan, 2009). Central to this endeavor is dialogue act classification, which categorizes the intention be- hind the user’s move (e.g., asking a question, providing declarative information). Automatic dia- logue act classification has been the focus of a large body of research, and a variety of approach- es, including sequential models (Stolcke et al., 2000), vector-based models (Sridhar, Bangalore, & Narayanan, 2009), and most recently, feature- enhanced latent semantic analysis (Di Eugenio, Xie, & Serafin, 2010), have shown promise. These models may be further improved by leveraging regularities of the dialogue from both linguistic and extra-linguistic sources. Users’ expressions of emotion are one such source. Human interaction has long been understood to include rich phenomena consisting of verbal and nonverbal cues, with facial expressions playing a vital role (Knapp & Hall, 2006; McNeill, 1992; Mehrabian, 2007; Russell, Bachorowski, & Fernandez-Dols, 2003; Schmidt & Cohn, 2001). While the importance of emotional expressions in dialogue is widely recognized, the majority of dia- logue act classification projects have focused either peripherally (or not at all) on emotion, such as by leveraging acoustic and prosodic features of spo- ken utterances to aid in online dialogue act classi- fication (Sridhar, Bangalore, & Narayanan, 2009). Other research on emotion in dialogue has in- volved detecting affect and adapting to it within a dialogue system (Forbes-Riley, Rotaru, Litman, & Tetreault, 2009; López-Cózar, Silovsky, & Griol, 2010), but this work has not explored leveraging affect information for automatic user dialogue act classification. Outside of dialogue, sentiment anal- ysis within discourse is an active area of research (López-Cózar et al., 2010), but it is generally lim- 1190 ited to modeling textual …

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