Enhancing Contrastive Learning with Noise-Guided Attack: Towards
  Continual Relation Extraction in the Wild
Ting Wu Jingyi Liu Rui Zheng Qi Zhang Tao Gui Xuanjing Huang
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.