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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 51–60 of 141 papers

TitleStatusHype
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion RecognitionCode1
EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTaCode1
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party ConversationsCode1
Supervised Adversarial Contrastive Learning for Emotion Recognition in ConversationsCode1
Attention-based Modeling for Emotion Detection and Classification in Textual Conversations—0
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation—0
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation—0
Emotion Dynamics Modeling via BERT—0
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations—0
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations—0
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