Continual relation extraction (RE) aims to learn constantly emerging relations while avoiding forgetting the learned relations. Existing works store a small number of typical samples to re-train the model for alleviating forgetting. However, repeatedly replaying these samples may cause the overfitting problem. We conduct an empirical study on existing works and observe that their performance is severely affected by analogous relations. To address this issue, we propose a novel continual extraction model for analogous relations. Specifically, we design memory-insensitive relation prototypes and memory augmentation to overcome the overfitting problem. We also introduce integrated training and focal knowledge distillation to enhance the performance on analogous relations. Experimental results show the superiority of our model and demonstrate its effectiveness in distinguishing analogous relations and overcoming overfitting.

翻译：持续关系抽取旨在学习不断涌现的新关系，同时避免遗忘已学关系。现有方法通过存储少量典型样本对模型进行重训练以缓解遗忘问题，但重复回放这些样本可能导致过拟合。我们对现有方法进行实证研究，发现其性能严重受到相似关系的干扰。为解决该问题，我们提出一种针对相似关系的新型持续抽取模型。具体而言，我们设计了内存不敏感的关系原型与内存增强机制来克服过拟合问题，同时引入集成训练与焦点知识蒸馏以提升对相似关系的处理性能。实验结果表明，我们的模型具有优越性，并在区分相似关系与克服过拟合方面展现了有效性。