@inproceedings{tan-etal-2022-doubly,
title = "Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation",
author = "Tan, Weiting and
Ding, Shuoyang and
Khayrallah, Huda and
Koehn, Philipp",
editor = "Duh, Kevin and
Guzm{\'a}n, Francisco",
booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
month = sep,
year = "2022",
address = "Orlando, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2022.amta-research.12",
pages = "157--174",
abstract = "Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserve the source-side meaning at the same time. To generate such samples, we propose a doubly-trained architecture that pairs two NMT models of opposite translation directions with a joint loss function, which combines the target-side attack and the source-side semantic similarity constraint. The results from our experiments across three different language pairs and two evaluation metrics show that these adversarial samples improve model robustness.",
}
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%0 Conference Proceedings
%T Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation
%A Tan, Weiting
%A Ding, Shuoyang
%A Khayrallah, Huda
%A Koehn, Philipp
%Y Duh, Kevin
%Y Guzmán, Francisco
%S Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
%D 2022
%8 September
%I Association for Machine Translation in the Americas
%C Orlando, USA
%F tan-etal-2022-doubly
%X Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make models robust, we generate adversarial augmentation samples that attack the model and preserve the source-side meaning at the same time. To generate such samples, we propose a doubly-trained architecture that pairs two NMT models of opposite translation directions with a joint loss function, which combines the target-side attack and the source-side semantic similarity constraint. The results from our experiments across three different language pairs and two evaluation metrics show that these adversarial samples improve model robustness.
%U https://aclanthology.org/2022.amta-research.12
%P 157-174
Markdown (Informal)
[Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation](https://aclanthology.org/2022.amta-research.12) (Tan et al., AMTA 2022)
ACL