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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 7180 of 141 papers

TitleStatusHype
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation0
M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation0
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation0
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AICode2
EmotionFlow: Capture the Dialogue Level Emotion TransitionsCode1
M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation0
COGMEN: COntextualized GNN based Multimodal Emotion recognitioNCode1
EmoCaps:Emotion Capsule based Model for Conversationl Emotion Recognition0
EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition0
MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in ConversationsCode1
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