EmoDamp: A Model for Speaker Emotion Inference in Dialogues with Bidirectional Emotion Damping Mechanism.

Emotion analysis in conversation has been a popular research topic in the natural language processing field. While much of the existing research has focused on emotion recognition in conversation, the emotion inference task in conversation is more challenging due to its specific prerequisites. In this paper, we propose EmoDamp, a novel model for the conversational emotion inference task. By referring to psychological theories, a bidirectional emotion damping network(BED Net) is designed to model the emotion generation mechanism. Furthermore, external commonsense knowledge is also used to supplement the prediction results to make them more consistent with authentic logic. Finally, a conditional random field (CRF) is used to capture the potential constraints of the emotion transfer process in conversation and simulate emotion contagion. Experimental results on two benchmark datasets demonstrate that EmoDamp outperforms the baseline model, achieving improved results on each emotion category and showcasing its effectiveness.