Continual Named Entity Recognition (CNER) is a burgeoning area, which
involves updating an existing model by incorporating new entity types
sequentially. Nevertheless, continual learning approaches are often severely
afflicted by catastrophic forgetting. This issue is intensified in CNER due to
the consolidation of old entity types from previous steps into the non-entity
type at each step, leading to what is known as the semantic shift problem of
the non-entity type. In this paper, we introduce a pooled feature distillation
loss that skillfully navigates the trade-off between retaining knowledge of old
entity types and acquiring new ones, thereby more effectively mitigating the
problem of catastrophic forgetting. Additionally, we develop a confidence-based
pseudo-labeling for the non-entity type, \emph{i.e.,} predicting entity types
using the old model to handle the semantic shift of the non-entity type.
Following the pseudo-labeling process, we suggest an adaptive re-weighting
type-balanced learning strategy to handle the issue of biased type
distribution. We carried out comprehensive experiments on ten CNER settings
using three different datasets. The results illustrate that our method
significantly outperforms prior state-of-the-art approaches, registering an
average improvement of $6.3$\% and $8.0$\% in Micro and Macro F1 scores,
respectively.

在连续命名实体识别（CNER）领域中，我们引入了一种精确平衡旧实体与新实体之间知识保留的池化特征蒸馏损失方法，以更有效地缓解灾难性遗忘问题，并利用基于置信度的伪标记方法解决非实体类型的语义转移问题。在此基础上，我们还提出了一种自适应重新加权类型平衡学习策略来处理类型分布不均衡问题。通过对十个 CNER 设置及三个不同数据集进行全面实验，结果显示我们的方法在 Micro F1 和 Macro F1 得分上相较于当前最先进的方法平均提高了 6.3% 和 8.0%。