# Cluster-Level Contrastive Learning for Emotion Recognition in Conversations

arXiv: https://arxiv.org/abs/2302.03508 · HTML: https://mlsanity.com/paper/2302.03508/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 74 references that are in the map.
- First posted on arXiv 7 Feb 2023.

## Builds on (most cited first, 30 of 39)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [Linguistic Knowledge and Transferability of Contextual Representations](https://mlsanity.com/paper/1903.08855/) (2019)
- [K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters](https://mlsanity.com/paper/2002.01808/) (2020)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [Contrastive Clustering](https://mlsanity.com/paper/2009.09687/) (2020)
- [DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations](https://mlsanity.com/paper/2006.03659/) (2020)
- [ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer](https://mlsanity.com/paper/2105.11741/) (2021)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [CoMatch: Semi-supervised Learning with Contrastive Graph Regularization](https://mlsanity.com/paper/2011.11183/) (2020)
- [COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://mlsanity.com/paper/2010.02795/) (2020)
- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [Self-Guided Contrastive Learning for BERT Sentence Representations](https://mlsanity.com/paper/2106.07345/) (2021)
- [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://mlsanity.com/paper/1708.04299/) (2017)
- [Directed Acyclic Graph Network for Conversational Emotion Recognition](https://mlsanity.com/paper/2105.12907/) (2021)
- [Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations](https://mlsanity.com/paper/1909.10681/) (2019)
- [DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition](https://mlsanity.com/paper/2012.08695/) (2020)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)

## Built on by (most cited first, 14 of 14)

- [Towards Interpretable Mental Health Analysis with Large Language Models](https://mlsanity.com/paper/2304.03347/) (2023)
- [EmoSphere++: Emotion-Controllable Zero-Shot Text-to-Speech via Emotion-Adaptive Spherical Vector](https://mlsanity.com/paper/2411.02625/) (2024)
- [Disentangled Variational Autoencoder for Emotion Recognition in Conversations](https://mlsanity.com/paper/2305.14071/) (2023)
- [Training A Small Emotional Vision Language Model for Visual Art Comprehension](https://mlsanity.com/paper/2403.11150/) (2024)
- [Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation](https://mlsanity.com/paper/2403.20289/) (2024)
- [Privileged Contrastive Pretraining for Multimodal Affect Modelling](https://mlsanity.com/paper/2508.03729/) (2025)
- [A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge](https://mlsanity.com/paper/2308.04811/) (2023)
- [StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal Contrast](https://mlsanity.com/paper/2410.10048/) (2024)
- [ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy](https://mlsanity.com/paper/2308.06450/) (2023)
- [LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks](https://mlsanity.com/paper/2312.03756/) (2023)
- [Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition](https://mlsanity.com/paper/2602.20530/) (2026)
- [Improving Emotion Recognition Accuracy with Personalized Clustering](https://mlsanity.com/paper/2410.03696/) (2024)
- [Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition](https://mlsanity.com/paper/2412.13541/) (2024)
- [Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation](https://mlsanity.com/paper/2508.19533/) (2025)

## Abstract (arXiv)

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