@inproceedings{tan-etal-2020-morphin,
title = "It`s Morphin' Time! {C}ombating Linguistic Discrimination with Inflectional Perturbations",
author = "Tan, Samson and
Joty, Shafiq and
Kan, Min-Yen and
Socher, Richard",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.263/",
doi = "10.18653/v1/2020.acl-main.263",
pages = "2920--2935",
abstract = "Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data."
}
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<abstract>Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data.</abstract>
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%0 Conference Proceedings
%T It‘s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations
%A Tan, Samson
%A Joty, Shafiq
%A Kan, Min-Yen
%A Socher, Richard
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F tan-etal-2020-morphin
%X Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data.
%R 10.18653/v1/2020.acl-main.263
%U https://aclanthology.org/2020.acl-main.263/
%U https://doi.org/10.18653/v1/2020.acl-main.263
%P 2920-2935
Markdown (Informal)
[It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations](https://aclanthology.org/2020.acl-main.263/) (Tan et al., ACL 2020)
ACL