# Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation

arXiv: https://arxiv.org/abs/2210.08713 · HTML: https://mlsanity.com/paper/2210.08713/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [Decoupled Contrastive Learning](https://mlsanity.com/paper/2110.06848/) (2021)
- [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)
- [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)
- [DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations](https://mlsanity.com/paper/2106.01978/) (2021)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection](https://mlsanity.com/paper/2106.01071/) (2021)
- [HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition](https://mlsanity.com/paper/1904.04446/) (2019)
- [CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation](https://mlsanity.com/paper/2108.11626/) (2021)
- [Contrast and Generation Make BART a Good Dialogue Emotion Recognizer](https://mlsanity.com/paper/2112.11202/) (2021)
- [Graph Based Network with Contextualized Representations of Turns in Dialogue](https://mlsanity.com/paper/2109.04008/) (2021)
- [Hybrid Curriculum Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2112.11718/) (2021)
- [Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition](https://mlsanity.com/paper/2003.01478/) (2020)
- [Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation](https://mlsanity.com/paper/2206.03173/) (2022)

## Built on by (most cited first, 30 of 31)

- [Is ChatGPT Equipped with Emotional Dialogue Capabilities?](https://mlsanity.com/paper/2304.09582/) (2023)
- [Towards Interpretable Mental Health Analysis with Large Language Models](https://mlsanity.com/paper/2304.03347/) (2023)
- [CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning](https://mlsanity.com/paper/2212.10341/) (2022)
- [Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations](https://mlsanity.com/paper/2306.01505/) (2023)
- [UniSA: Unified Generative Framework for Sentiment Analysis](https://mlsanity.com/paper/2309.01339/) (2023)
- [Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations](https://mlsanity.com/paper/2401.04883/) (2024)
- [Deep Emotion Recognition in Textual Conversations: A Survey](https://mlsanity.com/paper/2211.09172/) (2022)
- [Is ChatGPT a Good Multi-Party Conversation Solver?](https://mlsanity.com/paper/2310.16301/) (2023)
- [TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2401.12987/) (2024)
- [InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2309.11911/) (2023)
- [Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification](https://mlsanity.com/paper/2405.11524/) (2024)
- [Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition](https://mlsanity.com/paper/2407.21536/) (2024)
- [Transformers Go for the LOLs: Generating (Humourous) Titles from Scientific Abstracts End-to-End](https://mlsanity.com/paper/2212.10522/) (2022)
- [Disentangled Variational Autoencoder for Emotion Recognition in Conversations](https://mlsanity.com/paper/2305.14071/) (2023)
- [A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion](https://mlsanity.com/paper/2307.11584/) (2023)
- [Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition](https://mlsanity.com/paper/2310.14614/) (2023)
- [Leveraging Hierarchical Prototypes as the Verbalizer for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2411.14880/) (2024)
- [Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing](https://mlsanity.com/paper/2306.06601/) (2023)
- [From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue](https://mlsanity.com/paper/2308.12648/) (2023)
- [Long-Tail Learning with Rebalanced Contrastive Loss](https://mlsanity.com/paper/2312.01753/) (2023)
- [Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation](https://mlsanity.com/paper/2403.20289/) (2024)
- [Personality-affected Emotion Generation in Dialog Systems](https://mlsanity.com/paper/2404.07229/) (2024)
- [Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning](https://mlsanity.com/paper/2310.18930/) (2023)
- [Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations](https://mlsanity.com/paper/2404.16905/) (2024)
- [Can Large Language Models Generate Effective Datasets for Emotion Recognition in Conversations?](https://mlsanity.com/paper/2508.05474/) (2025)
- [SCoPE: Shift-Aware Speaker-Conditioned Priors for Emotion Recognition in Conversations](https://mlsanity.com/paper/2607.20445/) (2026)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy](https://mlsanity.com/paper/2308.06450/) (2023)
- [A Mixture-of-Experts Model for Multimodal Emotion Recognition in Conversations](https://mlsanity.com/paper/2602.23300/) (2026)
- [Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation](https://mlsanity.com/paper/2404.11141/) (2024)

## Abstract (arXiv)

Capturing emotions within a conversation plays an essential role in modern dialogue systems. However, the weak correlation between emotions and semantics brings many challenges to emotion recognition in conversation (ERC). Even semantically similar utterances, the emotion may vary drastically depending on contexts or speakers. In this paper, we propose a Supervised Prototypical Contrastive Learning (SPCL) loss for the ERC task. Leveraging the Prototypical Network, the SPCL targets at solving the imbalanced classification problem through contrastive learning and does not require a large batch size. Meanwhile, we design a difficulty measure function based on the distance between classes and introduce curriculum learning to alleviate the impact of extreme samples. We achieve state-of-the-art results on three widely used benchmarks. Further, we conduct analytical experiments to demonstrate the effectiveness of our proposed SPCL and curriculum learning strategy. We release the code at https://github.com/caskcsg/SPCL.