@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://preview.aclanthology.org/fix-sig-urls/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."
}