  Aspect-Based Sentiment Analysis (ABSA) deals with the extraction of
sentiments and their targets. Collecting labeled data for this task in order to
help neural networks generalize better can be laborious and time-consuming. As
an alternative, similar data to the real-world examples can be produced
artificially through an adversarial process which is carried out in the
embedding space. Although these examples are not real sentences, they have been
shown to act as a regularization method which can make neural networks more
robust. In this work, we apply adversarial training, which was put forward by
Goodfellow et al. (2014), to the post-trained BERT (BERT-PT) language model
proposed by Xu et al. (2019) on the two major tasks of Aspect Extraction and
Aspect Sentiment Classification in sentiment analysis. After improving the
results of post-trained BERT by an ablation study, we propose a novel
architecture called BERT Adversarial Training (BAT) to utilize adversarial
training in ABSA. The proposed model outperforms post-trained BERT in both
tasks. To the best of our knowledge, this is the first study on the application
of adversarial training in ABSA.
