Exploiting BERT for End-to-End Aspect-based Sentiment Analysis
Xin Yan Li, Lidong Bing, Wenxuan Zhang, Wai Pang Lam · 2019
In this paper, we investigate the modeling power of contextualized embeddings from pretrained language models, e.g.BERT, on the E2E-ABSA task.Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA.The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works.Besides, we also standardize the comparative study by consistently utilizing a hold-out development dataset for model selection, which is largely ignored by previous works.Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA. 1