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Emotion Recognition In Conversation On Cped
評価指標
Accuracy of Sentiment
Macro-F1 of Sentiment
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
| Paper Title | |||
|---|---|---|---|
| BERT+AVG+MLP | 51.50 | 48.02 | CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI |
| DialogXL | 51.24 | 46.96 | DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition |
| bcLSTM | 49.65 | 45.40 | Context-Dependent Sentiment Analysis in User-Generated Videos |
| TextRCNN | 49.13 | 37.95 | Recurrent Convolutional Neural Networks for Text Classification |
| BERT_{utt} | 48.96 | 45.18 | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding |
| TextCNN | 48.90 | 34.37 | Convolutional Neural Networks for Sentence Classification |
| FastText | 48.62 | 30.33 | Bag of Tricks for Efficient Text Classification |
| DialogueRNN | 48.57 | 44.11 | DialogueRNN: An Attentive RNN for Emotion Detection in Conversations |
| EmoBERTa | 48.09 | 44.60 | EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa |
| TextRNN | 47.89 | 37.07 | Recurrent Neural Network for Text Classification with Multi-Task Learning |
| DialogueGCN | 47.69 | 45.12 | DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation |
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