Adversarial Training for Aspect-Based Sentiment Analysis with BERT
Table 4: Aspect sentiment classification (ASC) results. Acc: Accuracy; MF1: Macro-F1.
['Domain  [BOLD] Methods', 'Laptop Acc', 'Laptop MF1', 'Rest14 Acc', 'Rest14 MF1']
From the ablation studies, we extract the best results of BERT-PT and compare them with those of BAT. Similar improvements can be seen in ASC results with an increase of +2.16 in MF1 score for restaurant compared to +0.81 for laptop which is due to the increase in the number of training epochs for restaurant domain since it exhibits better results with more training while the model reaches its peak performance for laptop domain in earlier training epochs. In addition, applying adversarial training improves the network’s performance in both tasks, though at different rates. While for laptop there are similar improvements in both tasks (+0.69 in AE, +0.61 in ASC), for restaurant we observe different enhancements (+0.81 in AE, +0.12 in ASC). This could be attributed to the fact that these are two different datasets whereas the laptop dataset is the same for both tasks. Furthermore, the perturbation size plays an important role in performance of the system. By choosing the appropriate ones, as was shown, better results are achieved.