Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation

• URL: http://arxiv.org/abs/2110.05691v1
• Date: Tue, 12 Oct 2021 02:23:00 GMT
• Title: Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation
• Authors: Weiting Tan, Shuoyang Ding, Huda Khayrallah, Philipp Koehn
• Abstract summary: We generate adversarial augmentation samples that attack the model and preserve the source-side semantic meaning. The results from our experiments show that these adversarial samples improve the model robustness.
• Score: 8.822338727711715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 semantic 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 the model robustness.

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