CDM-MBT: Context-Dependent Modeling With Memory Bank Traceback for Emotion Recognition in Conversation

Emotion Recognition in Conversation (ERC) is a key component in affective dialogue systems, which helps the system understand users’ emotions and generate empathetic responses. The typical approaches for modeling context have been to generate local representation of each utterance in a conversation and then perform contextual modeling of these. However, these approaches are insufficient in understanding the context due to lacking the ability to jointly capture both utterance- and conversation-level contextual information. To better model the context, we propose Context-Dependent Modeling (CDM) to capture context-dependent representation of the query utterance by utilizing the powerful representation capability of pre-trained language models. We input both the query utterance and its previous conversational context into RoBERTa and discard the need to model the context separately after obtaining the representation, as it already provides an effective embedding of such context. Besides, the emotion of the query utterance can be traced back to the stable mood of the speaker, which is inherited from previous emotional states. We design Memory Bank Traceback (MBT) to track and summarize the speaker’s emotional inertia. We treat MBT as an auxiliary information source that enriches the representation of the query utterance. Extensive experiments on four benchmark datasets demonstrate that CDM-MBT outperforms most baseline models.