Few-shot named entity recognition (NER) aims to recognize novel named
entities in low-resource domains utilizing existing knowledge. However, the
present few-shot NER models assume that the labeled data are all clean without
noise or outliers, and there are few works focusing on the robustness of the
cross-domain transfer learning ability to textual adversarial attacks in
Few-shot NER. In this work, we comprehensively explore and assess the
robustness of few-shot NER models under textual adversarial attack scenario,
and found the vulnerability of existing few-shot NER models. Furthermore, we
propose a robust two-stage few-shot NER method with Boundary Discrimination and
Correlation Purification (BDCP). Specifically, in the span detection stage, the
entity boundary discriminative module is introduced to provide a highly
distinguishing boundary representation space to detect entity spans. In the
entity typing stage, the correlations between entities and contexts are
purified by minimizing the interference information and facilitating
correlation generalization to alleviate the perturbations caused by textual
adversarial attacks. In addition, we construct adversarial examples for
few-shot NER based on public datasets Few-NERD and Cross-Dataset. Comprehensive
evaluations on those two groups of few-shot NER datasets containing adversarial
examples demonstrate the robustness and superiority of the proposed method.

本研究探索和评估少样本命名实体识别模型在文本对抗攻击场景下的鲁棒性，并提出了一个由边界判别和关联净化组成的鲁棒两阶段少样本命名实体识别方法。该方法通过引入边界判别模块和净化关联模块，提高了命名实体边界检测和实体类型判别的鲁棒性，并在 Few-NERD 和 Cross-Dataset 两个包含对抗样本的少样本命名实体识别数据集上进行了综合评估，展示了其鲁棒性和优越性。