@inproceedings{han-etal-2021-diverse,
title = "Diverse Adversaries for Mitigating Bias in Training",
author = "Han, Xudong and
Baldwin, Timothy and
Cohn, Trevor",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.239",
doi = "10.18653/v1/2021.eacl-main.239",
pages = "2760--2765",
abstract = "Adversarial learning can learn fairer and less biased models of language processing than standard training. However, current adversarial techniques only partially mitigate the problem of model bias, added to which their training procedures are often unstable. In this paper, we propose a novel approach to adversarial learning based on the use of multiple diverse discriminators, whereby discriminators are encouraged to learn orthogonal hidden representations from one another. Experimental results show that our method substantially improves over standard adversarial removal methods, in terms of reducing bias and stability of training.",
}
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%0 Conference Proceedings
%T Diverse Adversaries for Mitigating Bias in Training
%A Han, Xudong
%A Baldwin, Timothy
%A Cohn, Trevor
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F han-etal-2021-diverse
%X Adversarial learning can learn fairer and less biased models of language processing than standard training. However, current adversarial techniques only partially mitigate the problem of model bias, added to which their training procedures are often unstable. In this paper, we propose a novel approach to adversarial learning based on the use of multiple diverse discriminators, whereby discriminators are encouraged to learn orthogonal hidden representations from one another. Experimental results show that our method substantially improves over standard adversarial removal methods, in terms of reducing bias and stability of training.
%R 10.18653/v1/2021.eacl-main.239
%U https://aclanthology.org/2021.eacl-main.239
%U https://doi.org/10.18653/v1/2021.eacl-main.239
%P 2760-2765
Markdown (Informal)
[Diverse Adversaries for Mitigating Bias in Training](https://aclanthology.org/2021.eacl-main.239) (Han et al., EACL 2021)
ACL
- Xudong Han, Timothy Baldwin, and Trevor Cohn. 2021. Diverse Adversaries for Mitigating Bias in Training. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2760–2765, Online. Association for Computational Linguistics.