SeqAttack: On Adversarial Attacks for Named Entity Recognition

Walter Simoncini, Gerasimos Spanakis

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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 languageEnglish
Title of host publicationProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
PublisherThe Association for Computational Linguistics
Pages308-318
Number of pages11
DOIs
Publication statusPublished - 1 Nov 2021
Event2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations - Punta Cana, Dominican Republic and Online, Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021
https://2021.emnlp.org/

Conference

Conference2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Abbreviated titleEMNLP 2021
Country/TerritoryDominican Republic
CityPunta Cana
Period7/11/2111/11/21
Internet address

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