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        <title>Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation</title>
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            <title>Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)</title>
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            <namePart type="given">Francisco</namePart>
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    <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.</abstract>
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        <url>https://aclanthology.org/2022.amta-research.12</url>
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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)

• Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation (Tan et al., AMTA 2022)

ACL

• Weiting Tan, Shuoyang Ding, Huda Khayrallah, and Philipp Koehn. 2022. Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation. In Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), pages 157–174, Orlando, USA. Association for Machine Translation in the Americas.