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。

通过区分类似语义来提高持续关系提取

Improving Continual Relation Extraction by Distinguishing Analogous  Semantics

Continual relation extraction (CRE) aims to continually learn new relations
from a class-incremental data stream. CRE model usually suffers from
catastrophic forgetting problem, i.e., the performance of old relations
seriously degrades when the model learns new relations. Most previous work
attributes catastrophic forgetting to the corruption of the learned
representations as new relations come, with an implicit assumption that the CRE
models have adequately learned the old relations. In this paper, through
empirical studies we argue that this assumption may not hold, and an important
reason for catastrophic forgetting is that the learned representations do not
have good robustness against the appearance of analogous relations in the
subsequent learning process. To address this issue, we encourage the model to
learn more precise and robust representations through a simple yet effective
adversarial class augmentation mechanism (ACA), which is easy to implement and
model-agnostic. Experimental results show that ACA can consistently improve the
performance of state-of-the-art CRE models on two popular benchmarks.

通过实证研究，本文指出 Catastrophic Forgetting 问题的其中一个原因是在模型学习新关系时，旧关系的学习表示不够具有健壮性，而提出了一种称为 Adversarial Class Augmentation 的方法，它可以更精确和强健地学习表示，从而改善了 Continual Relation Extraction 模型的表现。