Emotion-Response Prediction model in multi-turn conversations

Abstract

Emotions are important in analyzing human conversations. The current response selection methods, however, do not handle emotions in response selection. This paper proposes a model that simultaneously learns emotions expressed with speakers' multi-turn utterances and their responses using predicted emotion sequence. Evaluation using the MELD dataset showed that our model achieved higher accuracies in predicting emotions and responses compared with the methods that learn response selections without emotion analysis do.