@inproceedings{das-paik-2022-resilience,
title = "Resilience of Named Entity Recognition Models under Adversarial Attack",
author = "Das, Sudeshna and
Paik, Jiaul",
editor = "Bartolo, Max and
Kirk, Hannah and
Rodriguez, Pedro and
Margatina, Katerina and
Thrush, Tristan and
Jia, Robin and
Stenetorp, Pontus and
Williams, Adina and
Kiela, Douwe",
booktitle = "Proceedings of the First Workshop on Dynamic Adversarial Data Collection",
month = jul,
year = "2022",
address = "Seattle, WA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dadc-1.1/",
doi = "10.18653/v1/2022.dadc-1.1",
pages = "1--6",
abstract = "Named entity recognition (NER) is a popular language processing task with wide applications. Progress in NER has been noteworthy, as evidenced by the F1 scores obtained on standard datasets. In practice, however, the end-user uses an NER model on their dataset out-of-the-box, on text that may not be pristine. In this paper we present four model-agnostic adversarial attacks to gauge the resilience of NER models in such scenarios. Our experiments on four state-of-the-art NER methods with five English datasets suggest that the NER models are over-reliant on case information and do not utilise contextual information well. As such, they are highly susceptible to adversarial attacks based on these features."
}
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%0 Conference Proceedings
%T Resilience of Named Entity Recognition Models under Adversarial Attack
%A Das, Sudeshna
%A Paik, Jiaul
%Y Bartolo, Max
%Y Kirk, Hannah
%Y Rodriguez, Pedro
%Y Margatina, Katerina
%Y Thrush, Tristan
%Y Jia, Robin
%Y Stenetorp, Pontus
%Y Williams, Adina
%Y Kiela, Douwe
%S Proceedings of the First Workshop on Dynamic Adversarial Data Collection
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, WA
%F das-paik-2022-resilience
%X Named entity recognition (NER) is a popular language processing task with wide applications. Progress in NER has been noteworthy, as evidenced by the F1 scores obtained on standard datasets. In practice, however, the end-user uses an NER model on their dataset out-of-the-box, on text that may not be pristine. In this paper we present four model-agnostic adversarial attacks to gauge the resilience of NER models in such scenarios. Our experiments on four state-of-the-art NER methods with five English datasets suggest that the NER models are over-reliant on case information and do not utilise contextual information well. As such, they are highly susceptible to adversarial attacks based on these features.
%R 10.18653/v1/2022.dadc-1.1
%U https://aclanthology.org/2022.dadc-1.1/
%U https://doi.org/10.18653/v1/2022.dadc-1.1
%P 1-6
Markdown (Informal)
[Resilience of Named Entity Recognition Models under Adversarial Attack](https://aclanthology.org/2022.dadc-1.1/) (Das & Paik, DADC 2022)
ACL