Firstly, we observe that BERT-Original, BERT-Soft, and BERT-Hard outperform all soft attention baselines (in the first part of Table 2), which demonstrates the effectiveness of fine-tuning the pre-trained model on the aspect-based sentiment classification task.
bERT-Original outperforms AF-LSTM(CONV) by 2.63%∼9.57%, bERT-Soft outperforms AF-LSTM(CONV) by 2.01%∼9.60% and bERT-Hard improves AF-LSTM(CONV) by 3.38%∼11.23% in terms of accuracy.