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.

该研究旨在学习新出现的关系，同时避免忘记学过的关系，提出了一种用于相似关系的新颖连续抽取模型，包括设计记忆无关的关系原型和记忆增强等方法，以克服过拟合问题，并引入集成训练和关键知识蒸馏来增强对相似关系的处理，实验结果表明该模型的优越性及对相似关系的区分能力。