Dual-View Learning for Conversational Emotion Recognition Through Context and Emotion-Shift Modeling

Conversational Emotion Recognition (CER) has recently been explored through conversational context modeling to learn the emotion distribution, i.e., the likelihood over emotion categories associated with each utterance. While these methods have shown promising results in emotion classification, they often focus on the interactions between utterances (utterance-view) and overlook shifts in the speaker's emotions (emotion-view). This emphasis on homogeneous view modeling limits their overall effectiveness. To address this limitation, we propose DVL-CER, a novel Dual-View Learning approach for CER. DVL-CER integrates both the utterance-view and emotion-view using two projection heads, enabling cross-view projection of emotion distributions. Our approach offers several key advantages: (1) We introduce an emotion-view that captures shifts in a speaker's emotions from initial to subsequent states within a conversation. This view enriches the conversation modeling and supports seamless integration with various CER baseline models. (2) Our dual-view projection learning strategy flexibly balances consistency and independence between the two heterogeneous views, promoting view-specific adaptation learning and incorporating the emotion verification capability within CER. We validate DVL-CER through extensive experiments on two widely-used datasets, IEMOCAP and EmoryNLP. The results demonstrate that DVL-CER achieves state-of-the-art performance, delivering robust and high-quality emotion distributions compared with existing CER methods and other dual-view learning strategies.