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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

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Showing 111–120 of 141 papers

TitleStatusHype
COIN: Conversational Interactive Networks for Emotion Recognition in Conversation—0
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection—0
UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause—0
CMATH: Cross-Modality Augmented Transformer with Hierarchical Variational Distillation for Multimodal Emotion Recognition in Conversation—0
A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog—0
Recurrent Neural Network for Text Classification with Multi-Task Learning—0
CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation—0
Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition—0
Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion—0
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum—0
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