Emotion Recognition In Conversation On 7
Metrics
Accuracy
Weighted F1
Results
Performance results of various models on this benchmark
Model Name | 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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