Continual Learning for Text Classification with Information Disentanglement Based Regularization
Open accessAbstract
Yufan Huang, Yanzhe Zhang, Jiaao Chen, Xuezhi Wang, Diyi Yang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.
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Rehearsal-based methods are not suitable for scenarios with data privacy concerns.
“Rehearsal-based methods (de Masson D'Autume et al., 2019; Huang et al., 2021), which rely on storing past task data in a buffer and replaying it during training, are not suitable for scenarios with data privacy concerns.”
From Orthogonal Subspace Learning for Language Model Continual Learning (2023) · cites this paper as Huang et al., 2021
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References 48 · Cited by 74
- Authors
- Huang, Y., Zhang, Y., Chen, J., Wang, X., & Yang, D.
- Affiliations
- Georgia Institute of Technology (US)
- Zhejiang University (CN)
- Google (United States) (US)
- Venue
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2021
- Pages
- 2736-2746
- OpenAlex
- W3171057731
- Cited by
- 74
- References
- 48
- Citations by year
- 2026: 9 · 2025: 19 · 2024: 11 · 2023: 18 · 2022: 14 · 2021: 3
- FWCI
- 5.31
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- continual learning, text classification, regularization, catastrophic forgetting, domain adaptation
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