Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
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. | An Affect-Enriched Dialogue Act Classification Model for Task-Oriented Dialogue Kristy Joseph F. Eun Young Robert James C. Elizabeth Grafsgaard Ha Phillips 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 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. 1 Introduction Dialogue systems aim to engage users in rich adaptive natural language conversation. For these systems understanding the role of a user s utterance 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 behind the user s move e.g. asking a question providing declarative information . Automatic dialogue act classification has been the focus of a 1190 large body of research and a variety of approaches 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 .
