The principle of continual relation extraction~(CRE) involves adapting to emerging novel relations while preserving od knowledge. While current endeavors in CRE succeed in preserving old knowledge, they tend to fail when exposed to contaminated data streams. We assume this is attributed to their reliance on an artificial hypothesis that the data stream has no annotation errors, which hinders real-world applications for CRE. Considering the ubiquity of noisy labels in real-world datasets, in this paper, we formalize a more practical learning scenario, termed as \textit{noisy-CRE}. Building upon this challenging setting, we develop a noise-resistant contrastive framework named as \textbf{N}oise-guided \textbf{a}ttack in \textbf{C}ontrative \textbf{L}earning~(NaCL) to learn incremental corrupted relations. Compared to direct noise discarding or inaccessible noise relabeling, we present modifying the feature space to match the given noisy labels via attacking can better enrich contrastive representations. Extensive empirical validations highlight that NaCL can achieve consistent performance improvements with increasing noise rates, outperforming state-of-the-art baselines.

翻译：持续关系抽取（CRE）的核心在于适应新出现的关系类型，同时保留已有知识。然而，当前CRE方法虽能成功保持旧知识，但在面对受污染的数据流时往往失效。我们推测这源于其依赖一个人为假设——数据流不存在标注错误，而这阻碍了CRE在现实场景中的应用。鉴于真实数据集中噪声标签的普遍性，本文提出一种更贴近实际的学习场景，即“噪声持续关系抽取”（noisy-CRE）。基于这一挑战性设定，我们开发了名为“噪声引导对比攻击学习”（NaCL）的抗噪对比框架，用于学习增量式受损关系。与直接丢弃噪声或难以实现的噪声重新标注不同，我们提出通过攻击方式修改特征空间以匹配给定噪声标签，从而更有效地丰富对比表示。大量实证验证表明，随着噪声率的增加，NaCL能实现持续的性能提升，并显著优于现有最优基线方法。