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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 4150 of 141 papers

TitleStatusHype
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion RecognitionCode1
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in ConversationCode1
DialogueRNN: An Attentive RNN for Emotion Detection in ConversationsCode1
KNOT: Knowledge Distillation using Optimal Transport for Solving NLP TasksCode1
DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion RecognitionCode1
Directed Acyclic Graph Network for Conversational Emotion RecognitionCode1
InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language ModelsCode1
A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in ConversationsCode1
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological KnowledgeCode1
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