AutoML-Emo: Automatic Knowledge Selection Using Congruent Effect for Emotion Identification in Conversations

Abstract

Emotion recognition in conversations (ERC) has wide applications in medical care, human-computer interaction, and other fields. Unlike the general task of emotion analysis, humans usually rely on context and commonsense knowledge to convey emotions in conversations. Only when the model can connect and fully utilize a large-scale commonsense knowledge base, it can better understand latent contents in conversations. Unfortunately, there is no available knowledge selection mechanism to address such knowledge needs and to make sure the system is not flooded with irrelevant commonsense knowledge. Therefore, we propose an AutoML strategy based on emotion congruent effect to select suitable knowledge and models, called AutoML-Emo. Global exploration and local exploitation-based selection mechanisms (G&LESM) are used for automatic knowledge selection. The transformer-based architecture search (TAS) is applied to model selection, the selected transformer-based model is employed to incorporate knowledge and capture context information in conversations. The experimental results show that AutoML-Emo can effectively enhance external knowledge in different sizes and domain datasets. Moreover, the selected transformer-based model derived from TAS is superior to the most advanced models.