@inproceedings{pereira-etal-2024-prior,
title = "Prior Knowledge-Guided Adversarial Training",
author = "Pereira, Lis and
Cheng, Fei and
She, Wan Jou and
Asahara, Masayuki and
Kobayashi, Ichiro",
editor = "Zhao, Chen and
Mosbach, Marius and
Atanasova, Pepa and
Goldfarb-Tarrent, Seraphina and
Hase, Peter and
Hosseini, Arian and
Elbayad, Maha and
Pezzelle, Sandro and
Mozes, Maximilian",
booktitle = "Proceedings of the 9th Workshop on Representation Learning for NLP (RepL4NLP-2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.repl4nlp-1.5",
pages = "51--57",
abstract = "We introduce a simple yet effective Prior Knowledge-Guided ADVersarial Training (PKG-ADV) algorithm to improve adversarial training for natural language understanding. Our method simply utilizes task-specific label distribution to guide the training process. By prioritizing the use of prior knowledge of labels, we aim to generate more informative adversarial perturbations. We apply our model to several challenging temporal reasoning tasks. Our method enables a more reliable and controllable data training process than relying on randomized adversarial perturbation. Albeit simple, our method achieved significant improvements in these tasks. To facilitate further research, we will release the code and models.",
}
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%0 Conference Proceedings
%T Prior Knowledge-Guided Adversarial Training
%A Pereira, Lis
%A Cheng, Fei
%A She, Wan Jou
%A Asahara, Masayuki
%A Kobayashi, Ichiro
%Y Zhao, Chen
%Y Mosbach, Marius
%Y Atanasova, Pepa
%Y Goldfarb-Tarrent, Seraphina
%Y Hase, Peter
%Y Hosseini, Arian
%Y Elbayad, Maha
%Y Pezzelle, Sandro
%Y Mozes, Maximilian
%S Proceedings of the 9th Workshop on Representation Learning for NLP (RepL4NLP-2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F pereira-etal-2024-prior
%X We introduce a simple yet effective Prior Knowledge-Guided ADVersarial Training (PKG-ADV) algorithm to improve adversarial training for natural language understanding. Our method simply utilizes task-specific label distribution to guide the training process. By prioritizing the use of prior knowledge of labels, we aim to generate more informative adversarial perturbations. We apply our model to several challenging temporal reasoning tasks. Our method enables a more reliable and controllable data training process than relying on randomized adversarial perturbation. Albeit simple, our method achieved significant improvements in these tasks. To facilitate further research, we will release the code and models.
%U https://aclanthology.org/2024.repl4nlp-1.5
%P 51-57
Markdown (Informal)
[Prior Knowledge-Guided Adversarial Training](https://aclanthology.org/2024.repl4nlp-1.5) (Pereira et al., RepL4NLP-WS 2024)
ACL
- Lis Pereira, Fei Cheng, Wan Jou She, Masayuki Asahara, and Ichiro Kobayashi. 2024. Prior Knowledge-Guided Adversarial Training. In Proceedings of the 9th Workshop on Representation Learning for NLP (RepL4NLP-2024), pages 51–57, Bangkok, Thailand. Association for Computational Linguistics.