Robust Few-Shot Named Entity Recognition with Boundary Discrimination
  and Correlation Purification
Xiaojun Xue Chunxia Zhang Tianxiang Xu Zhendong Niu
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.