  A key challenge for Emotion Recognition in Conversations (ERC) is to
distinguish semantically similar emotions. Some works utilise Supervised
Contrastive Learning (SCL) which uses categorical emotion labels as supervision
signals and contrasts in high-dimensional semantic space. However, categorical
labels fail to provide quantitative information between emotions. ERC is also
not equally dependent on all embedded features in the semantic space, which
makes the high-dimensional SCL inefficient. To address these issues, we propose
a novel low-dimensional Supervised Cluster-level Contrastive Learning (SCCL)
method, which first reduces the high-dimensional SCL space to a
three-dimensional affect representation space Valence-Arousal-Dominance (VAD),
then performs cluster-level contrastive learning to incorporate measurable
emotion prototypes. To help modelling the dialogue and enriching the context,
we leverage the pre-trained knowledge adapters to infuse linguistic and factual
knowledge. Experiments show that our method achieves new state-of-the-art
results with 69.81% on IEMOCAP, 65.7% on MELD, and 62.51% on DailyDialog
datasets. The analysis also proves that the VAD space is not only suitable for
ERC but also interpretable, with VAD prototypes enhancing its performance and
stabilising the training of SCCL. In addition, the pre-trained knowledge
adapters benefit the performance of the utterance encoder and SCCL. Our code is
available at: https://github.com/SteveKGYang/SCCL
