Cluster-Level Contrastive Learning for Emotion Recognition in Conversations

Abstract

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

arXiv commentAccepted by IEEE Transactions on Affective Computing

References in corpus (5)

• Overcoming catastrophic forgetting in neural networks
• Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
• Pre-trained Models for Natural Language Processing: A Survey
• EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa
• Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach

Cited by in corpus (6)

• Towards Interpretable Mental Health Analysis with Large Language Models
• Disentangled Variational Autoencoder for Emotion Recognition in Conversations
• ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy
• EmoSphere++: Emotion-Controllable Zero-Shot Text-to-Speech via Emotion-Adaptive Spherical Vector
• A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge
• Privileged Contrastive Pretraining for Multimodal Affect Modelling