CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation

Nishant Kambhatla, Logan Born, Anoop Sarkar


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.17.pdf
Code
 protonish/cipherdaug-nmt