DDCL: Dual-Space Weight Decoupled Contrastive Learning Framework with Emotion Prototypes for Emotion Recognition in Conversation
Authors: Yukun Cao, Luobin Huang, Yijia Tang


Reviewer paper summaries: The paper introduces a novel approach to emotion recognition in conversation (ERC), focusing on addressing the challenge of confusion between similar sentiment labels. The authors propose a dual-space weighted decoupled contrastive learning framework (DDCL) that incorporates Poincaré space to increase the distance between similar semantic emotions, thereby alleviating label confusion. The framework also uses decoupled contrastive loss and high-quality emotion prototypes computed from clustering centers to enhance model performance. Experimental results on three datasets demonstrate the effectiveness of the proposed method, particularly in handling similar semantic sentiments and recognizing difficult samples.
Summary of strengths: 1. The paper's use of Poincaré space in conjunction with Euclidean space for emotion representation is innovative and potentially transformative for ERC tasks. This dual-space approach allows for better differentiation between emotions that are semantically similar.
2. The implementation of decoupled contrastive loss is a promising technique to improve the model's ability to distinguish between various labeled utterance representations, which is crucial for ERC.
3. The method for computing emotion prototypes using clustering centers adds robustness to the model, providing a strong foundation for data enhancement and improving overall performance.
Summary of weaknesses: 1. More comparative methods should be introduced. For example, 
Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation
Improving Contrastive Learning in Emotion Recognition in Conversation via Data Augmentation and Decoupled Neutral Emotion
Disentangled Variational Autoencoder for Emotion Recognition in Conversations
it is also better to add dailydialog dataset's performance.  While the paper compares DDCL with several other contrastive learning methods, it could benefit from a more extensive comparison with state-of-the-art ERC models that do not necessarily rely on contrastive learning.
More comprehensive ablation studies could provide further insights into the contribution of each component of the proposed framework.
2.  The paper lacks detailed explanation of the implementation specifics, such as the exact hyperparameters used in the experiments and the choice of clustering algorithm for computing emotion prototypes.  In addition, it is better to open source the codes.
3. The paper builds upon existing contrastive learning approaches, introducing dual-space and decoupled contrastive learning. While the combination of these techniques is novel, the individual techniques themselves are not entirely new. More justification of why this specific combination is effective would strengthen the novelty aspect.
4. While the paper discusses the use of emotional prototypes for data augmentation, a more detailed analysis of how these prototypes evolve during training and their impact on the model's performance could strengthen the paper.
5. A thorough error analysis, identifying common types of misclassifications and their causes, could provide valuable insights for future improvements.
Comments, suggestions and typos: None
Overall assessment scores: 2.5
Metareview score: 2
Metareview: The paper presents Dual-Space Weight Decoupled Contrastive Learning (DDCL) for Emotion Recognition in Conversation (ERC). This method utilizes both Euclidean and Poincaré spaces for emotional representations to distinguish similar emotions. This method also integrates decoupled contrastive loss and high-quality emotion prototypes obtained from clustering centers. Experimental results show the proposed method.
Summary of reasons to publish: 1. The application of Poincaré space for emotion representation is new in the ERC field. ‘
2. DDCL shows better performance. 
3. The dual-space method and the implementation of decoupled contrastive loss improve the performance on distinguishing semantically similar emotions.