Attack Named Entity Recognition by Entity Boundary Interference
This addresses the vulnerability of NER models to adversarial attacks, which is a critical issue for NLP applications relying on entity recognition, though it is incremental in the field of adversarial attacks.
The paper tackles the problem of robustness in Named Entity Recognition (NER) by proposing a novel one-word modification attack called Virtual Boundary Attack (ViBA), which achieves 70%-90% attack success rates on state-of-the-art language models like RoBERTa and DeBERTa while being faster than previous methods.
Named Entity Recognition (NER) is a cornerstone NLP task while its robustness has been given little attention. This paper rethinks the principles of NER attacks derived from sentence classification, as they can easily violate the label consistency between the original and adversarial NER examples. This is due to the fine-grained nature of NER, as even minor word changes in the sentence can result in the emergence or mutation of any entities, resulting in invalid adversarial examples. To this end, we propose a novel one-word modification NER attack based on a key insight, NER models are always vulnerable to the boundary position of an entity to make their decision. We thus strategically insert a new boundary into the sentence and trigger the Entity Boundary Interference that the victim model makes the wrong prediction either on this boundary word or on other words in the sentence. We call this attack Virtual Boundary Attack (ViBA), which is shown to be remarkably effective when attacking both English and Chinese models with a 70%-90% attack success rate on state-of-the-art language models (e.g. RoBERTa, DeBERTa) and also significantly faster than previous methods.