Supervised neural machine translation based on data augmentation and improved training & inference process

Yixuan Tong, Liang Liang, Boyan Liu, Shanshan Jiang, Bin Dong


Abstract
This is the second time for SRCB to participate in WAT. This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019. We participated in ASPEC tasks and submitted results on English-Japanese, Japanese-English, Chinese-Japanese, and Japanese-Chinese four language pairs. We employed the Transformer model as the baseline and experimented relative position representation, data augmentation, deep layer model, ensemble. Experiments show that all these methods can yield substantial improvements.
Anthology ID:
D19-5218
Volume:
Proceedings of the 6th Workshop on Asian Translation
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Toshiaki Nakazawa, Chenchen Ding, Raj Dabre, Anoop Kunchukuttan, Nobushige Doi, Yusuke Oda, Ondřej Bojar, Shantipriya Parida, Isao Goto, Hidaya Mino
Venue:
WAT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
147–151
Language:
URL:
https://aclanthology.org/D19-5218
DOI:
10.18653/v1/D19-5218
Bibkey:
Cite (ACL):
Yixuan Tong, Liang Liang, Boyan Liu, Shanshan Jiang, and Bin Dong. 2019. Supervised neural machine translation based on data augmentation and improved training & inference process. In Proceedings of the 6th Workshop on Asian Translation, pages 147–151, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Supervised neural machine translation based on data augmentation and improved training & inference process (Tong et al., WAT 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5218.pdf