NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis

NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis

我們運用MacBERT模型在CVAT與CVAS資料集微調使其適用於ROCLING 2021的評測任務，並比較MacBERT與BERT和RoBERTa這兩個不同的模型，在Valence與Arousal維度上的效能差異。我們以平均絕對誤差(MAE)與關係係數(r)作為評分標準，在測試資料上能夠在Valence達到MAE與r分別為0.611與0.904；而在Arousal達到MAE與r分別為0.938與0.549的效能。

We use the MacBERT transformers and fine-tune them to ROCLING-2021 shared tasks using the CVAT and CVAS data. We compare the performance of MacBERT with the other two transformers BERT and RoBERTa in the valence and arousal dimensions, respectively. MAE and correlation coefficient (r) were used as evaluation metrics. On ROCLING-2021 test set, our used MacBERT model achieves 0.611 of MAE and 0.904 of r in the valence dimensions; and 0.938 of MAE and 0.549 of r in the arousal dimension.