Abstract
In this paper, we propose a new adversarial augmentation method for Neural Machine Translation (NMT). The main idea is to minimize the vicinal risk over virtual sentences sampled from two vicinity distributions, in which the crucial one is a novel vicinity distribution for adversarial sentences that describes a smooth interpolated embedding space centered around observed training sentence pairs. We then discuss our approach, AdvAug, to train NMT models using the embeddings of virtual sentences in sequence-to-sequence learning. Experiments on Chinese-English, English-French, and English-German translation benchmarks show that AdvAug achieves significant improvements over theTransformer (up to 4.9 BLEU points), and substantially outperforms other data augmentation techniques (e.g.back-translation) without using extra corpora.- Anthology ID:
- 2020.acl-main.529
- Original:
- 2020.acl-main.529v1
- Version 2:
- 2020.acl-main.529v2
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5961–5970
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.acl-main.529/
- DOI:
- 10.18653/v1/2020.acl-main.529
- Cite (ACL):
- Yong Cheng, Lu Jiang, Wolfgang Macherey, and Jacob Eisenstein. 2020. AdvAug: Robust Adversarial Augmentation for Neural Machine Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5961–5970, Online. Association for Computational Linguistics.
- Cite (Informal):
- AdvAug: Robust Adversarial Augmentation for Neural Machine Translation (Cheng et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.acl-main.529.pdf
- Data
- WMT 2014