@inproceedings{boreshban-etal-2023-robustqa,
title = "{R}obust{QA}: A Framework for Adversarial Text Generation Analysis on Question Answering Systems",
author = "Boreshban, Yasaman and
Mirbostani, Seyed Morteza and
Ahmadi, Seyedeh Fatemeh and
Shojaee, Gita and
Kamani, Fatemeh and
Ghassem-Sani, Gholamreza and
Mirroshandel, Seyed Abolghasem",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.24",
doi = "10.18653/v1/2023.emnlp-demo.24",
pages = "274--285",
abstract = "Question answering (QA) systems have reached human-level accuracy; however, these systems are not robust enough and are vulnerable to adversarial examples. Recently, adversarial attacks have been widely investigated in text classification. However, there have been few research efforts on this topic in QA. In this article, we have modified the attack algorithms widely used in text classification to fit those algorithms for QA systems. We have evaluated the impact of various attack methods on QA systems at character, word, and sentence levels. Furthermore, we have developed a new framework, named RobustQA, as the first open-source toolkit for investigating textual adversarial attacks in QA systems. RobustQA consists of seven modules: Tokenizer, Victim Model, Goals, Metrics, Attacker, Attack Selector, and Evaluator. It currently supports six different attack algorithms. Furthermore, the framework simplifies the development of new attack algorithms in QA. The source code and documentation of RobustQA are available at https://github.com/mirbostani/RobustQA.",
}
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<abstract>Question answering (QA) systems have reached human-level accuracy; however, these systems are not robust enough and are vulnerable to adversarial examples. Recently, adversarial attacks have been widely investigated in text classification. However, there have been few research efforts on this topic in QA. In this article, we have modified the attack algorithms widely used in text classification to fit those algorithms for QA systems. We have evaluated the impact of various attack methods on QA systems at character, word, and sentence levels. Furthermore, we have developed a new framework, named RobustQA, as the first open-source toolkit for investigating textual adversarial attacks in QA systems. RobustQA consists of seven modules: Tokenizer, Victim Model, Goals, Metrics, Attacker, Attack Selector, and Evaluator. It currently supports six different attack algorithms. Furthermore, the framework simplifies the development of new attack algorithms in QA. The source code and documentation of RobustQA are available at https://github.com/mirbostani/RobustQA.</abstract>
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%0 Conference Proceedings
%T RobustQA: A Framework for Adversarial Text Generation Analysis on Question Answering Systems
%A Boreshban, Yasaman
%A Mirbostani, Seyed Morteza
%A Ahmadi, Seyedeh Fatemeh
%A Shojaee, Gita
%A Kamani, Fatemeh
%A Ghassem-Sani, Gholamreza
%A Mirroshandel, Seyed Abolghasem
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F boreshban-etal-2023-robustqa
%X Question answering (QA) systems have reached human-level accuracy; however, these systems are not robust enough and are vulnerable to adversarial examples. Recently, adversarial attacks have been widely investigated in text classification. However, there have been few research efforts on this topic in QA. In this article, we have modified the attack algorithms widely used in text classification to fit those algorithms for QA systems. We have evaluated the impact of various attack methods on QA systems at character, word, and sentence levels. Furthermore, we have developed a new framework, named RobustQA, as the first open-source toolkit for investigating textual adversarial attacks in QA systems. RobustQA consists of seven modules: Tokenizer, Victim Model, Goals, Metrics, Attacker, Attack Selector, and Evaluator. It currently supports six different attack algorithms. Furthermore, the framework simplifies the development of new attack algorithms in QA. The source code and documentation of RobustQA are available at https://github.com/mirbostani/RobustQA.
%R 10.18653/v1/2023.emnlp-demo.24
%U https://aclanthology.org/2023.emnlp-demo.24
%U https://doi.org/10.18653/v1/2023.emnlp-demo.24
%P 274-285
Markdown (Informal)
[RobustQA: A Framework for Adversarial Text Generation Analysis on Question Answering Systems](https://aclanthology.org/2023.emnlp-demo.24) (Boreshban et al., EMNLP 2023)
ACL