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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 3140 of 141 papers

TitleStatusHype
Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion RecognitionCode1
Exploiting Unsupervised Data for Emotion Recognition in ConversationsCode1
Contrast and Generation Make BART a Good Dialogue Emotion RecognizerCode1
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in ConversationCode1
Convolutional Neural Networks for Sentence ClassificationCode1
A Hierarchical Transformer with Speaker Modeling for Emotion Recognition in ConversationCode1
Contextual Information and Commonsense Based Prompt for Emotion Recognition in ConversationCode1
Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion RecognitionCode1
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion RecognitionCode1
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in ConversationsCode1
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