  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.
