Continuous Relation Extraction (CRE) aims to incrementally learn relation knowledge from a non-stationary stream of data. Since the introduction of new relational tasks can overshadow previously learned information, catastrophic forgetting becomes a significant challenge in this domain. Current replay-based training paradigms prioritize all data uniformly and train memory samples through multiple rounds, which would result in overfitting old tasks and pronounced bias towards new tasks because of the imbalances of the replay set. To handle the problem, we introduce the DecouPled CRE (DP-CRE) framework that decouples the process of prior information preservation and new knowledge acquisition. This framework examines alterations in the embedding space as new relation classes emerge, distinctly managing the preservation and acquisition of knowledge. Extensive experiments show that DP-CRE significantly outperforms other CRE baselines across two datasets.

翻译：持续关系抽取（Continual Relation Extraction, CRE）旨在从非平稳数据流中增量式地学习关系知识。由于新关系任务的引入可能掩盖先前学习的知识，灾难性遗忘成为该领域的关键挑战。当前基于回放的训练范式对所有数据统一优先级，并通过多轮训练记忆样本，这会因回放集的不平衡而导致旧任务过拟合，并显著偏向新任务。为解决此问题，我们提出解耦持续关系抽取（DecouPled CRE, DP-CRE）框架，将先验信息保持与新知识获取过程解耦。该框架通过分析新关系类别出现时嵌入空间的变化，明确管理知识的保持与获取。大量实验表明，DP-CRE在两个数据集上的性能显著优于其他CRE基线方法。