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The principle of continual relation extraction~(CRE) involves adapting toemerging novel relations while preserving od knowledge. While current endeavorsin CRE succeed in preserving old knowledge, they tend to fail when exposed tocontaminated data streams. We assume this is attributed to their reliance on anartificial hypothesis that the data stream has no annotation errors, whichhinders real-world applications for CRE. Considering the ubiquity of noisylabels in real-world datasets, in this paper, we formalize a more practicallearning scenario, termed as \textit{noisy-CRE}. Building upon this challengingsetting, 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 todirect noise discarding or inaccessible noise relabeling, we present modifyingthe feature space to match the given noisy labels via attacking can betterenrich contrastive representations. Extensive empirical validations highlightthat NaCL can achieve consistent performance improvements with increasing noiserates, outperforming state-of-the-art baselines.

Enhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in the Wild