Abstract
We propose a novel data-augmentation technique for neural machine translation based on ROT-k ciphertexts. ROT-k is a simple letter substitution cipher that replaces a letter in the plaintext with the kth letter after it in the alphabet. We first generate multiple ROT-k ciphertexts using different values of k for the plaintext which is the source side of the parallel data. We then leverage this enciphered training data along with the original parallel data via multi-source training to improve neural machine translation. Our method, CipherDAug, uses a co-regularization-inspired training procedure, requires no external data sources other than the original training data, and uses a standard Transformer to outperform strong data augmentation techniques on several datasets by a significant margin. This technique combines easily with existing approaches to data augmentation, and yields particularly strong results in low-resource settings.- Anthology ID:
- 2022.acl-long.17
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 201–218
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.17
- DOI:
- 10.18653/v1/2022.acl-long.17
- Cite (ACL):
- Nishant Kambhatla, Logan Born, and Anoop Sarkar. 2022. CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 201–218, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation (Kambhatla et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.acl-long.17.pdf
- Code
- protonish/cipherdaug-nmt