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    <abstract>We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while maintaining gold answers. Despite phenomenal progress on general adversarial attacks, few works have investigated the vulnerability and attack specifically for QA models. In this work, we first explore the biases in the existing models and discover that they mainly rely on keyword matching between the question and context, and ignore the relevant contextual relations for answer prediction.Based on two biases above, TASA attacks the target model in two folds: (1) lowering the model’s confidence on the gold answer with a perturbed answer sentence; (2) misguiding the model towards a wrong answer with a distracting answer sentence. Equipped with designed beam search and filtering methods, TASA can generate more effective attacks than existing textual attack methods while sustaining the quality of contexts, in extensive experiments on five QA datasets and human evaluations.</abstract>
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%0 Conference Proceedings
%T TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
%A Cao, Yu
%A Li, Dianqi
%A Fang, Meng
%A Zhou, Tianyi
%A Gao, Jun
%A Zhan, Yibing
%A Tao, Dacheng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F cao-etal-2022-tasa
%X We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while maintaining gold answers. Despite phenomenal progress on general adversarial attacks, few works have investigated the vulnerability and attack specifically for QA models. In this work, we first explore the biases in the existing models and discover that they mainly rely on keyword matching between the question and context, and ignore the relevant contextual relations for answer prediction.Based on two biases above, TASA attacks the target model in two folds: (1) lowering the model’s confidence on the gold answer with a perturbed answer sentence; (2) misguiding the model towards a wrong answer with a distracting answer sentence. Equipped with designed beam search and filtering methods, TASA can generate more effective attacks than existing textual attack methods while sustaining the quality of contexts, in extensive experiments on five QA datasets and human evaluations.
%R 10.18653/v1/2022.emnlp-main.821
%U https://aclanthology.org/2022.emnlp-main.821
%U https://doi.org/10.18653/v1/2022.emnlp-main.821
%P 11975-11992

Markdown (Informal)

[TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack](https://aclanthology.org/2022.emnlp-main.821) (Cao et al., EMNLP 2022)

• TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack (Cao et al., EMNLP 2022)

ACL

• Yu Cao, Dianqi Li, Meng Fang, Tianyi Zhou, Jun Gao, Yibing Zhan, and Dacheng Tao. 2022. TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11975–11992, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.