Continual relation extraction (CRE) models aim at handling emerging new
relations while avoiding catastrophically forgetting old ones in the streaming
data. Though improvements have been shown by previous CRE studies, most of them
only adopt a vanilla strategy when models first learn representations of new
relations. In this work, we point out that there exist two typical biases after
training of this vanilla strategy: classifier bias and representation bias,
which causes the previous knowledge that the model learned to be shaded. To
alleviate those biases, we propose a simple yet effective classifier
decomposition framework that splits the last FFN layer into separated previous
and current classifiers, so as to maintain previous knowledge and encourage the
model to learn more robust representations at this training stage. Experimental
results on two standard benchmarks show that our proposed framework
consistently outperforms the state-of-the-art CRE models, which indicates that
the importance of the first training stage to CRE models may be underestimated.
Our code is available at this https URL

许多先前的 CRE 研究仅采用传统策略，但是本文提出，此策略在训练后会出现分类器偏差和表示偏差，导致模型遗忘已学习的知识，因此我们提出了一个简单而有效的分类器分解框架，实验结果表明我们的方法性能优于现有的方法。