Exploiting BERT for End-to-End Aspect-based Sentiment Analysis

About

In this paper, we investigate the modeling power of contextualized embeddings from pre-trained 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 validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA.

Xin Li, Lidong Bing, Wenxuan Zhang, Wai Lam · 2019

Related benchmarks

Task	Dataset	Result
Named Entity Recognition	Wnut 2017	F1 Score55.76	91
Named Entity Recognition	Conll 2003	F1 Score91.76	86
Aspect-Term Sentiment Analysis	LAPTOP SemEval 2014 (test)	Macro-F161.12	69
Named Entity Recognition	WeiboNER	F1 Score69.53	27
Entity-Level Financial Sentiment Analysis	EFSA	F1 Score73.77	23
Sequence Labeling	Restaurant 14	F1 Score74	20
Sequence Labeling	Restaurant 16	F1 Score71.47	20
Sequence Labeling	Restaurant15	F1 Score61.58	20
Sequence Labeling	Laptop14	F1 Score61.33	20
End-to-End Aspect-Based Sentiment Analysis	REST SemEval 2015 2016 (test)	F1 Score74.72	10