Improving Robustness of Machine Translation with Synthetic Noise

Vaibhav Vaibhav, Sumeet Singh, Craig Stewart, Graham Neubig


Abstract
Modern Machine Translation (MT) systems perform remarkably well on clean, in-domain text. However most of the human generated text, particularly in the realm of social media, is full of typos, slang, dialect, idiolect and other noise which can have a disastrous impact on the accuracy of MT. In this paper we propose methods to enhance the robustness of MT systems by emulating naturally occurring noise in otherwise clean data. Synthesizing noise in this manner we are ultimately able to make a vanilla MT system more resilient to naturally occurring noise, partially mitigating loss in accuracy resulting therefrom.
Anthology ID:
N19-1190
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1916–1920
Language:
URL:
https://aclanthology.org/N19-1190
DOI:
10.18653/v1/N19-1190
Bibkey:
Cite (ACL):
Vaibhav Vaibhav, Sumeet Singh, Craig Stewart, and Graham Neubig. 2019. Improving Robustness of Machine Translation with Synthetic Noise. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1916–1920, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Improving Robustness of Machine Translation with Synthetic Noise (Vaibhav et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1190.pdf
Code
 MysteryVaibhav/robust_mtnt
Data
MTNT