Positive Emotion Elicitation in Chat-Based Dialogue Systems

• Published in: IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2019-04, Vol.27 (4), p.866-877
• Main Authors: Lubis, Nurul, Sakti, Sakriani, Yoshino, Koichiro, Nakamura, Satoshi
• Format: Article
• Language: English
• Subjects: Affective computing; Appraisal; chat-based dialogue system; Crowdsourcing; emotion elicitation; Emotion recognition; Emotional factors; Emotions; Natural language processing; Neural networks; Retrieval; Speech processing; Speech recognition; Voice recognition
• ISSN: 2329-9290; 2329-9304
• DOI: 10.1109/TASLP.2019.2900910

We aim to draw on an important overlooked potential of affective dialogue systems-their application to promote positive emotional states, similar to that of emotional support between humans. This can be achieved by eliciting a more positive emotional valence throughout a dialogue system interaction, i.e., positive emotion elicitation. Existing works on emotion elicitation have not yet paid attention to the emotional benefit for the users. Moreover, a positive emotion elicitation corpus does not yet exist despite the growing number of emotion-rich corpora. Towards this goal, first, we propose a response retrieval approach for positive emotion elicitation by utilizing examples of emotion appraisal from a dialogue corpus. Second, we efficiently construct a corpus using the proposed retrieval method, by replacing responses in a dialogue with those that elicit a more positive emotion. We validate the corpus through crowdsourcing to ensure its quality. Finally, we propose a novel neural network architecture for an emotion-sensitive neural chat-based dialogue system, optimized on the constructed corpus to elicit positive emotion. Objective and subjective evaluations show that the proposed methods result in dialogue responses that are more natural and elicit a more positive emotional response. Further analyses of the results are discussed in this paper.