ntust-nlp-2 at ROCLING-2021 Shared Task: BERT-based semantic analyzer with word-level information

ntust-nlp-2 at ROCLING-2021 Shared Task: BERT-based semantic analyzer with word-level information

本論文提出基於BERT架構之維度式情感辨識器，透過增加基於詞層級的資訊，我們的模型在「ROCLING 2021共享任務：教學文本之維度式情感辨識」四項指標中拿到了三項最佳成績。通過一連串的實驗，我們比較了不同預訓練方法對於結果的影響，也證明了我們基於預訓練模型所提出的方法，顯著的改進了模型的表現，最後我們針對實驗結果，提出關於模型和資料集的深入分析與討論。

In this paper, we proposed a BERT-based dimensional semantic analyzer, which is designed by incorporating with word-level information. Our model achieved three of the best results in four metrics on“ROCLING 2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts”. We conducted a series of experiments to compare the effectiveness of different pre-trained methods. Besides, the results also proofed that our method can significantly improve the performances than classic methods. Based on the experiments, we also discussed the impact of model architectures and datasets.