Continual relation extraction (CRE) requires the model to continually learn
new relations from class-incremental data streams. In this paper, we propose a
Frustratingly easy but Effective Approach (FEA) method with two learning stages
for CRE: 1) Fast Adaption (FA) warms up the model with only new data. 2)
Balanced Tuning (BT) finetunes the model on the balanced memory data. Despite
its simplicity, FEA achieves comparable (on TACRED or superior (on FewRel)
performance compared with the state-of-the-art baselines. With careful
examinations, we find that the data imbalance between new and old relations
leads to a skewed decision boundary in the head classifiers over the pretrained
encoders, thus hurting the overall performance. In FEA, the FA stage unleashes
the potential of memory data for the subsequent finetuning, while the BT stage
helps establish a more balanced decision boundary. With a unified view, we find
that two strong CRE baselines can be subsumed into the proposed training
pipeline. The success of FEA also provides actionable insights and suggestions
for future model designing in CRE.

提出了一个名为 FEA 的连续关系抽取方法，其中包含两个学习阶段：快速适应和平衡微调。该方法适用于处理类增量数据流，并在 TACRED 或 FewRel 上实现了与最先进的基线相当或更高的性能。通过对数据不平衡的仔细检查，我们发现 FA 阶段可以放大内存数据的潜力，而 BT 阶段有助于建立更平衡的决策边界。此方法提供了可操作的见解和建议，为未来的 CRE 模型设计提供启示。