Abstract
Named Entity Recognition is a fundamental task in information extraction and is an essential element for various Natural Language Processing pipelines. Adversarial attacks have been shown to greatly affect the performance of text classification systems but knowledge about their effectiveness against named entity recognition models is limited. This paper investigates the effectiveness and portability of adversarial attacks from text classification to named entity recognition and the ability of adversarial training to counteract these attacks. We find that character-level and word-level attacks are the most effective, but adversarial training can grant significant protection at little to no expense of standard performance. Alongside our results, we also release SeqAttack, a framework to conduct adversarial attacks against token classification models (used in this work for named entity recognition) and a companion web application to inspect and cherry pick adversarial examples.
Original language | English |
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Title of host publication | Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations |
Publisher | The Association for Computational Linguistics |
Pages | 308-318 |
Number of pages | 11 |
ISBN (Print) | 9781955917117 |
DOIs | |
Publication status | Published - 1 Nov 2021 |
Event | 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations - Punta Cana, Dominican Republic and Online, Punta Cana, Dominican Republic Duration: 7 Nov 2021 → 11 Nov 2021 https://2021.emnlp.org/ |
Conference
Conference | 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations |
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Abbreviated title | EMNLP 2021 |
Country/Territory | Dominican Republic |
City | Punta Cana |
Period | 7/11/21 → 11/11/21 |
Internet address |