Papers › Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks
Zixuan Ke, Hu Xu, Bing Liu
This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks. Although some CL techniques have been proposed for document sentiment classification, we are not aware of any CL work on ASC. A CL system that incrementally learns a sequence of ASC tasks should address the following two issues: (1) transfer knowledge learned from previous tasks to the new task to help it learn a better model, and (2) maintain the performance of the models for previous tasks so that they are not forgotten. This paper proposes a novel capsule network based model called B-CL to address these issues. B-CL markedly improves the ASC performance on both the new task and the old tasks via forward and backward knowledge transfer. The effectiveness of B-CL is demonstrated through extensive experiments.
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Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Continual Learning | 20Newsgroup (10 tasks) | B-CL | F1 - macro | 0.9504 | #4 of 6 | Archive leaderboard | report |
| Continual Learning | ASC (19 tasks) | B-CL | F1 - macro | 0.8140 | #3 of 15 | Archive leaderboard | report |
| Continual Learning | DSC (10 tasks) | B-CL | F1 - macro | 0.7651 | #5 of 6 | 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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