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%0 Conference Proceedings
%T Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification
%A Pruksachatkun, Yada
%A Krishna, Satyapriya
%A Dhamala, Jwala
%A Gupta, Rahul
%A Chang, Kai-Wei
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F pruksachatkun-etal-2021-robustness
%R 10.18653/v1/2021.findings-acl.294
%U https://aclanthology.org/2021.findings-acl.294
%U https://doi.org/10.18653/v1/2021.findings-acl.294
%P 3320-3331

Markdown (Informal)

[Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification](https://aclanthology.org/2021.findings-acl.294) (Pruksachatkun et al., Findings 2021)

• Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification (Pruksachatkun et al., Findings 2021)

ACL

• Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, and Kai-Wei Chang. 2021. Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 3320–3331, Online. Association for Computational Linguistics.