NICT Self-Training Approach to Neural Machine Translation at NMT-2018

Kenji Imamura, Eiichiro Sumita


Abstract
This paper describes the NICT neural machine translation system submitted at the NMT-2018 shared task. A characteristic of our approach is the introduction of self-training. Since our self-training does not change the model structure, it does not influence the efficiency of translation, such as the translation speed. The experimental results showed that the translation quality improved not only in the sequence-to-sequence (seq-to-seq) models but also in the transformer models.
Anthology ID:
W18-2713
Volume:
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–115
URL:
https://www.aclweb.org/anthology/W18-2713
DOI:
10.18653/v1/W18-2713
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
https://www.aclweb.org/anthology/W18-2713.pdf