PeTracker: Poincaré-Based Dual-Strategy Emotion Tracker for Emotion Recognition in Conversation

With the increasing use of interactive applications, the importance of Emotion recognition in conversation (ERC) is growing. Current research in the ERC domain mainly emphasizes the extraction of contextual information. However, challenges arise due to multi-turn conversation scenarios and the natural transformation of emotions, particularly in identifying subtle emotion transfers. Moreover, emotions exhibit nonlinear characteristics in semantic spaces, leading to potential confusion when discerning similar semantic emotions in the Euclidean semantic space. To address these issues, this study proposes a Poincaré-based dual-strategy emotion tracker for emotion recognition in conversation (PeTracker), which introduces the hyperbolic space representation in the ERC domain. Based on the spatial properties of the hyperbolic space representation to capture the nonlinear relationships among features, PeTracker encompasses two learning strategies. Poincaré emotional geometry curriculum learning (PGCL) and Poincaré emotional stratification contrastive learning (PSCL). In PGCL, the similarity of emotion labels is effectively discerned using the Poincaré distance, quantifying emotion transfer distances and facilitating the identification of subtle emotion transfers in utterance. In PSCL, PeTracker extracts and adapts multi-level features, mapping them to the Poincaré ball space to build emotion prototype-based contrastive learning. This process enhances the model’s ability to distinguish between similar emotion labels. while alleviating potential label confusion issues. Experimental results on three general datasets demonstrate that PeTracker achieves optimal or near-optimal performance. Furthermore, the study investigates the role and impact of the Poincaré ball in differentiating similar emotions.