论文标题
更少的是：以令人沮丧但有效的方法重新思考最新的持续关系提取模型
Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach
论文作者
论文摘要
持续的关系提取（CRE）要求模型不断从类信息流中学习新关系。在本文中，我们提出了一种令人沮丧的简便但有效的方法（FEA）方法，其中有两个学习阶段的CRE：1）快速适应（FA）仅使用新数据加热模型。 2）平衡调谐（BT）列出平衡内存数据上的模型。尽管它很简单，但FEA与最先进的基准相比，FEA取得了可比性（在盗版或优越（在较少的情况下）性能。经过仔细的检查。我们发现，新关系和旧关系之间的数据失衡会导致校长偏斜的决策界限，导致偏向于预期的型号的编码，从而在fea fore for fiea for fiea for fea for fie for fea for。 BT阶段有助于建立一个更加平衡的决策边界，我们发现可以将两个强大的CRE基线包含在提议的训练管道中。
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.
