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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 91100 of 141 papers

TitleStatusHype
M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation0
M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation0
Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation0
MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting0
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations0
TED: Turn Emphasis with Dialogue Feature Attention for Emotion Recognition in Conversation0
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation0
DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation0
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis0
Modeling both context- and speaker-sensitive dependence for emotion detection in multi-speaker conversations0
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