  Emotion recognition in conversations (ERC) is a rapidly evolving task within
the natural language processing community, which aims to detect the emotions
expressed by speakers during a conversation. Recently, a growing number of ERC
methods have focused on leveraging supervised contrastive learning (SCL) to
enhance the robustness and generalizability of learned features. However,
current SCL-based approaches in ERC are impeded by the constraint of large
batch sizes and the lack of compatibility with most existing ERC models. To
address these challenges, we propose an efficient and model-agnostic SCL
framework named Supervised Sample-Label Contrastive Learning with Soft-HGR
Maximal Correlation (SSLCL), which eliminates the need for a large batch size
and can be seamlessly integrated with existing ERC models without introducing
any model-specific assumptions. Specifically, we introduce a novel perspective
on utilizing label representations by projecting discrete labels into dense
embeddings through a shallow multilayer perceptron, and formulate the training
objective to maximize the similarity between sample features and their
corresponding ground-truth label embeddings, while minimizing the similarity
between sample features and label embeddings of disparate classes. Moreover, we
innovatively adopt the Soft-HGR maximal correlation as a measure of similarity
between sample features and label embeddings, leading to significant
performance improvements over conventional similarity measures. Additionally,
multimodal cues of utterances are effectively leveraged by SSLCL as data
augmentations to boost model performances. Extensive experiments on two ERC
benchmark datasets, IEMOCAP and MELD, demonstrate the compatibility and
superiority of our proposed SSLCL framework compared to existing
state-of-the-art SCL methods. Our code is available at
\url{https://github.com/TaoShi1998/SSLCL}.
