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

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

2 Oct 2019WS 2019 11arXiv:1910.00883archive 2025-07-28

Xin Li, Lidong Bing, Wenxuan Zhang, Wai Lam

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.

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Code

lixin4ever/BERT-E2E-ABSA officialmentioned in paperpytorchApache-2.0 report

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Tasks

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA)Model Selection Sentiment Analysis

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Aspect-Based Sentiment Analysis (ABSA)	SemEval 2014 Task 4 Laptop	BERT-E2E-ABSA	F1	61.12	#5 of 9	Archive leaderboard	report
Aspect-Based Sentiment Analysis (ABSA)	SemEval 2014 Task 4 Subtask 1+2	BERT-E2E-ABSA	F1	61.12	#7 of 10	Archive leaderboard	report
Sentiment Analysis	SemEval 2014 Task 4 Subtask 1+2	BERT-E2E-ABSA	F1	61.12	#5 of 8	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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