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홈
SOTA
Emotion Recognition In Conversation
Emotion Recognition In Conversation On 7
Emotion Recognition In Conversation On 7
평가 지표
Accuracy
Weighted F1
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Accuracy
Weighted F1
Paper Title
Repository
MMGCN
79.75
79.72
MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation
GraphSmile
86.53
86.52
Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition
MM-DFN
80.91
80.83
MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations
M3Net
83.67
83.57
Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in Conversation
Joyful
-
85.70
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition
DialogueCRN
81.34
81.28
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations
SACL-LSTM
80.70
80.74
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations
COGMEN
-
84.50
COGMEN: COntextualized GNN based Multimodal Emotion recognitioN
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