FSE: Continual Learning for Named Entity Recognition by Fast-Slow Experts
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Computer Science > Computation and Language
arXiv:2607.22075 (cs)
[Submitted on 24 Jul 2026]
Title:FSE: Continual Learning for Named Entity Recognition by Fast-Slow Experts
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Abstract:Continual Learning for Named Entity Recognition (CLNER) enable models to incrementally learn new entity types without forgetting previously acquired ones. However, existing methods suffer from catastrophic forgetting and insufficient exploitation of shared information across tasks. This paper proposes FSE, a Fast-Slow Experts enhanced span-based NER model for CLNER. The shared fast expert learns token-level links to efficiently filter out unlikely spans, while the task-specific slow expert performs span classification only on the remaining candidates. It stabilizes learning by promoting knowledge sharing across tasks and maintains plasticity by reducing learning burden at each task. A length-decay negative sampling strategy to mitigate span imbalance is also introduced. Extensive experiments on OntoNotes and FewNERD synthestic datasets demonstrate that FSE achieves state-of-the-art performance in CLNER scenarios, with effectiveness of each component, empirical evidence of faster convergence and expected functionality of both experts.
| Comments: | Preprint submitted to Pattern Recognition Letters |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.22075 [cs.CL] |
| (or arXiv:2607.22075v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.22075
arXiv-issued DOI via DataCite (pending registration)
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