@inproceedings{kovatchev-etal-2022-longhorns,
title = "longhorns at {DADC} 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.",
author = "Kovatchev, Venelin and
Chatterjee, Trina and
Govindarajan, Venkata S and
Chen, Jifan and
Choi, Eunsol and
Chronis, Gabriella and
Das, Anubrata and
Erk, Katrin and
Lease, Matthew and
Li, Junyi Jessy and
Wu, Yating and
Mahowald, Kyle",
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-1.5",
doi = "10.18653/v1/2022.dadc-1.5",
pages = "41--52",
abstract = "Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability. Here, we describe the approach of the team {``}longhorns{''} on Task 1 of the The First Workshop on Dynamic Adversarial Data Collection (DADC), which asked teams to manually fool a model on an Extractive Question Answering task. Our team finished first (pending validation), with a model error rate of 62{\%}. We advocate for a systematic, linguistically informed approach to formulating adversarial questions, and we describe the results of our pilot experiments, as well as our official submission.",
}
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<abstract>Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability. Here, we describe the approach of the team “longhorns” on Task 1 of the The First Workshop on Dynamic Adversarial Data Collection (DADC), which asked teams to manually fool a model on an Extractive Question Answering task. Our team finished first (pending validation), with a model error rate of 62%. We advocate for a systematic, linguistically informed approach to formulating adversarial questions, and we describe the results of our pilot experiments, as well as our official submission.</abstract>
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%0 Conference Proceedings
%T longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.
%A Kovatchev, Venelin
%A Chatterjee, Trina
%A Govindarajan, Venkata S.
%A Chen, Jifan
%A Choi, Eunsol
%A Chronis, Gabriella
%A Das, Anubrata
%A Erk, Katrin
%A Lease, Matthew
%A Li, Junyi Jessy
%A Wu, Yating
%A Mahowald, Kyle
%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 kovatchev-etal-2022-longhorns
%X Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability. Here, we describe the approach of the team “longhorns” on Task 1 of the The First Workshop on Dynamic Adversarial Data Collection (DADC), which asked teams to manually fool a model on an Extractive Question Answering task. Our team finished first (pending validation), with a model error rate of 62%. We advocate for a systematic, linguistically informed approach to formulating adversarial questions, and we describe the results of our pilot experiments, as well as our official submission.
%R 10.18653/v1/2022.dadc-1.5
%U https://aclanthology.org/2022.dadc-1.5
%U https://doi.org/10.18653/v1/2022.dadc-1.5
%P 41-52
Markdown (Informal)
[longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.](https://aclanthology.org/2022.dadc-1.5) (Kovatchev et al., DADC 2022)
ACL
- Venelin Kovatchev, Trina Chatterjee, Venkata S Govindarajan, Jifan Chen, Eunsol Choi, Gabriella Chronis, Anubrata Das, Katrin Erk, Matthew Lease, Junyi Jessy Li, Yating Wu, and Kyle Mahowald. 2022. longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.. In Proceedings of the First Workshop on Dynamic Adversarial Data Collection, pages 41–52, Seattle, WA. Association for Computational Linguistics.