Papers › Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for...
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification
Alexander Rietzler, Sebastian Stabinger, Paul Opitz, Stefan Engl
Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect-Based Sentiment Analysis (ABSA), which has many applications e.g. in e-commerce, where data and insights from reviews can be leveraged to create value for businesses and customers. Recently, deep transfer-learning methods have been applied successfully to a myriad of Natural Language Processing (NLP) tasks, including ATSC. Building on top of the prominent BERT language model, we approach ATSC using a two-step procedure: self-supervised domain-specific BERT language model finetuning, followed by supervised task-specific finetuning. Our findings on how to best exploit domain-specific language model finetuning enable us to produce new state-of-the-art performance on the SemEval 2014 Task 4 restaurants dataset. In addition, to explore the real-world robustness of our models, we perform cross-domain evaluation. We show that a cross-domain adapted BERT language model performs significantly better than strong baseline models like vanilla BERT-base and XLNet-base. Finally, we conduct a case study to interpret model prediction errors.
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Code
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Code Syntology ran Syntology
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Tasks
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 | BERT-ADA | Laptop (Acc) | 80.23 | #10 of 48 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | BERT-ADA | Mean Acc (Restaurant + Laptop) | 84.06 | #10 of 48 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | BERT-ADA | Restaurant (Acc) | 87.89 | #10 of 48 | 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
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