Challenge: Emotion recognition in conversation research suffers from data imbalance and the presence of similar linguistic expressions for different emotions.
Approach: They propose a Contrast-Enhanced Prompt-Tuning framework that transforms an ERC task into a Masked Language Modeling task and generates the emotion for each utterance in the conversation.
Outcome: The proposed framework outperforms the state-of-the-art methods on all three benchmark datasets and excels in recognizing minority emotions.

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Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition (2023.findings-emnlp)

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Challenge: Emotion Recognition in Conversation (ERC) models are often expensive to train and fine-tune .
Approach: They propose a derivative-free optimization method for few-shot conversational emotion recognition that leverages sharable cross-task knowledge by exploiting external knowledge from other source tasks.
Outcome: The proposed method improves on few-shot scenarios and zero-shot transfers on five different contextual conversation datasets.
Improving Contrastive Learning in Emotion Recognition in Conversation via Data Augmentation and Decoupled Neutral Emotion (2024.eacl-long)

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Challenge: Existing methods to model context of utterances and speaker are inadequate . despite the improvements, there are still intrinsic challenges in the ERC dataset .
Approach: They propose a supervised contrastive learning method specifically oriented for ERC task . they employ a data augmentation method emulating the emotion dynamics in a conversation and a method addressing the predominance and ambiguity of neutral emotion.
Outcome: The proposed method emulates the emotion dynamics in a conversation and addresses the predominance and the ambiguity of neutral emotion.
Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation (2024.findings-naacl)

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Challenge: Emotion Recognition in Conversation (ERC) is a task that aims to identify the emotions behind each utterance in a conversation.
Approach: They propose an Emotion-Anchored Contrastive Learning framework that generates more distinguishable utterance representations for similar emotions.
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JoPR: Joint Emotion Perception and Reasoning for Conversational Emotion Recognition (2026.acl-long)

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Challenge: Existing methods for ERC lack human-like emotion reasoning and discrimination between similar emotions.
Approach: They propose a multi-dimension curriculum with long CoT fine-tuning to clone human-like emotion reasoning for conversational emotion recognition.
Outcome: The proposed model outperforms existing methods on three widely used datasets and shows that it is more intuitive and more accurate.
Enhancing Emotion Recognition in Conversation via Multi-view Feature Alignment and Memorization (2023.findings-emnlp)

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Challenge: Emotion recognition in conversation (ERC) is an advanced capability of conversational AI systems.
Approach: They propose a semi-parametric paradigm for Emotion Recognition in conversation that uses supervised contrastive learning to align semantic-view and context-view features.
Outcome: The proposed model achieves state-of-the-art on four widely used benchmarks.
ESCP: Enhancing Emotion Recognition in Conversation with Speech and Contextual Prefixes (2024.lrec-main)

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Challenge: Emotion Recognition in Conversation (ERC) aims to analyze the speaker’s emotional state in a conversation.
Approach: They propose to combine a directed acyclic graph and contextual prefixes to model historical utterances in a conversation and incorporate a contextual prefixed containing sentiment and semantics of historical .
Outcome: The proposed model achieves state-of-the-art (SOTA) performance on several public benchmarks.
Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation (2022.emnlp-main)

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Challenge: Existing methods to capture emotions in conversation (ERC) lack the correlation between emotions and semantics, resulting in many challenges.
Approach: They propose a Supervised Prototypical Contrastive Learning (SPCL) loss for the ERC task . they use a Prototype Network to leverage the supervised contrastive learning approach .
Outcome: The proposed approach outperforms CoG-BART's proposed approach on three widely used benchmarks and shows that it is effective on multiple scenarios.
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations (2020.coling-main)

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Challenge: Emotion recognition in conversations (ERC) is a task that aims to recognize the emotion of each utterance in conversations.
Approach: They propose an iterative emotion interaction network which uses iterativly predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction.
Outcome: The proposed method retains state-of-the-art performance on two datasets and achieves high accuracy.
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations (2023.acl-long)

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Challenge: Existing methods to recognize emotions have limitations in discovering the intrinsic structure of data relevant to emotion labels, and struggle to extract generalized and robust representations.
Approach: They propose a supervised adversarial contrastive learning framework for learning class-spread structured representations in a controlled manner.
Outcome: The proposed framework can extract generalized and robust representations on three datasets and achieves state-of-the-art performance.
Global-Local Modeling with Prompt-Based Knowledge Enhancement for Emotion Inference in Conversation (2023.findings-eacl)

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Challenge: Existing studies on emotion recognition focus on recognizing emotions through a speaker’s utterance, while research on emotion inference predicts emotions of addressees through previous utterations.
Approach: They propose a global-local modeling method based on recurrent neural networks and pre-trained language models to do emotion inference in conversation.
Outcome: The proposed method achieves state-of-the-art on three datasets.

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