@inproceedings{yang-etal-2024-attack,
    title = "Attack Named Entity Recognition by Entity Boundary Interference",
    author = "Yang, Yifei  and
      Wu, Hongqiu  and
      Zhao, Hai",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.153",
    pages = "1734--1744",
    abstract = "Named Entity Recognition (NER) is a cornerstone natural language processing task while its robustness has been given little attention. This paper rethinks the principles of the conventional text attack, as they can easily violate the label consistency between the original and adversarial NER samples. 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 entity, producing invalid adversarial samples. 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 victim model to make a wrong recognition either on this boundary word or on other words in the sentence. We call this attack \textit{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, and also significantly faster than previous methods.",
}

Named Entity Recognition (NER) is a cornerstone natural language processing task while its robustness has been given little attention. This paper rethinks the principles of the conventional text attack, as they can easily violate the label consistency between the original and adversarial NER samples. 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 entity, producing invalid adversarial samples. 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 victim model to make a wrong recognition either on this boundary word or on other words in the sentence. We call this attack \textit{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, and also significantly faster than previous methods.
Yang, Yifei Wu, Hongqiu Zhao, Hai
Attack Named Entity Recognition by Entity Boundary Interference