基于多头自注意力机制与MLP-Interactor的多模态情感分析
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林宜山,左景,卢树华
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Multimodal sentiment analysis based on multi-head self-attention mechanism and MLP-Interactor
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Yishan LIN,Jing ZUO,Shuhua LU
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表 4 在CMU-MOSI数据集上的消融实验结果 |
Tab.4 Result of ablation experiment on CMU-MOSI dataset |
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方法 | MAE | corr | A2 | A7 | F1 | BERT | 0.799 | 0.746 | 80.80/82.87 | 40.77 | 80.91/82.90 | DeBERTa | 1.154 | 0.486 | 66.96/68.06 | 28.86 | 66.93/67.93 | RoBERTa | 0.664 | 0.824 | 83.63/85.65 | 45.53 | 83.65/85.64 | DistilBERT | 0.754 | 0.768 | 81.40/83.54 | 40.48 | 81.48/83.55 | ALBERT | 0.928 | 0.670 | 76.93/79.00 | 34.67 | 77.10/79.08 | w/o Intra-Modality Interaction | 0.613 | 0.851 | 86.90/89.10 | 47.92 | 86.90/89.10 | w/o MLP-Interactor | 0.590 | 0.868 | 85.57/87.54 | 50.00 | 85.66/87.59 | w/o audio | 0.635 | 0.853 | 86.46/88.16 | 44.79 | 86.48/88.15 | w/o video | 0.583 | 0.861 | 87.35/89.42 | 49.55 | 87.45/89.47 | w/o text | 1.460 | 0.052 | 45.95/47.47 | 14.58 | 50.86/52.50 | 本文模型 | 0.575 | 0.868 | 87.60/89.60 | 52.23 | 87.70/89.60 |
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