FSE：通过快速-慢速专家进行命名实体识别的持续学习
FSE: Continual Learning for Named Entity Recognition by Fast-Slow Experts
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- School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
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中文总结 AI 辅助
研究命名实体识别的持续学习问题，提出FSE模型，通过快速-慢速专家结合及长度衰减负采样策略，在跨任务知识共享、减轻学习负担等方面发挥作用，在CLNER场景中取得了最优性能。
中文摘要 AI 辅助
命名实体识别的持续学习（CLNER）使模型能够增量学习新实体类型而不忘记先前学到的类型。然而，现有方法存在灾难性遗忘和跨任务共享信息利用不足的问题。本文提出FSE，一种用于CLNER的基于跨度的快速-慢速专家增强NER模型。共享的快速专家学习令牌级链接以有效过滤不可能的跨度，而特定于任务的慢速专家仅对其余候选进行跨度分类。它通过促进跨任务知识共享来稳定学习，并通过减轻每个任务的学习负担来保持可塑性。还引入了一种长度衰减负采样策略来减轻跨度不平衡。在OntoNotes和FewNERD合成数据集上的大量实验表明，FSE在CLNER场景中实现了最新性能，每个组件都有效，有更快收敛的经验证据以及两个专家的预期功能。
英文摘要
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.
