ntust-nlp-1 at ROCLING-2021 Shared Task: Educational Texts Dimensional Sentiment Analysis using Pretrained Language Models

ntust-nlp-1 at ROCLING-2021 Shared Task: Educational Texts Dimensional Sentiment Analysis using Pretrained Language Models

本研究為Rocling 2021共同任務：教育文本的維度式情感分析之成果報告。為了分析中文文本的情緒效價(Valence)與喚起程度(Arousal)，本研究基於當前流行的預訓練語言模型BERT與近期基於全詞遮蔽(Whole Word Masking)進行的MacBERT，觀察模型在不同設定下的預測成果，並比較BERT與MacBERT在中文文本情緒預測效能的差異。我們發現，相較於BERT，MacBERT可以在驗證集上獲得些許的效能提升。因此，我們將數個使用不同訓練方法所得的預測模型進行預測結果平均，作為最終的輸出。

This technical report aims at the ROCLING 2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts. In order to predict the affective states of Chinese educational texts, we present a practical framework by employing pre-trained language models, such as BERT and MacBERT. Several valuable observations and analyses can be drawn from a series of experiments. From the results, we find that MacBERT-based methods can deliver better results than BERT-based methods on the verification set. Therefore, we average the prediction results of several models obtained using different settings as the final output.