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Emotion Recognition in Conversation

Given the transcript of a conversation along with speaker information of each constituent utterance, the ERC task aims to identify the emotion of each utterance from several pre-defined emotions. Formally, given the input sequence of N number of utterances [(u1, p1), (u2, p2), . . . , (uN , pN )], where each utterance ui = [ui,1, ui,2, . . . , ui,T ] consists of T words ui,j and spoken by party pi, the task is to predict the emotion label ei of each utterance ui. .

Papers

Showing 1120 of 141 papers

TitleStatusHype
Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression RecognitionCode1
Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in ConversationCode1
Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion RecognitionCode1
TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in ConversationCode1
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion RecognitionCode1
A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in ConversationsCode1
InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language ModelsCode1
UniSA: Unified Generative Framework for Sentiment AnalysisCode1
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion RecognitionCode1
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party ConversationsCode1
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