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.- Anthology ID:
- 2022.amta-research.12
- Volume:
- Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
- Month:
- September
- Year:
- 2022
- Address:
- Orlando, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 157–174
- Language:
- URL:
- https://aclanthology.org/2022.amta-research.12
- DOI:
- Cite (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.
- Cite (Informal):
- Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation (Tan et al., AMTA 2022)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2022.amta-research.12.pdf
- Code
- steventan0110/NMTModelAttack
- Data
- WMT 2014