NTT’s Neural Machine Translation Systems for WMT 2018

Makoto Morishita, Jun Suzuki, Masaaki Nagata


Abstract
This paper describes NTT’s neural machine translation systems submitted to the WMT 2018 English-German and German-English news translation tasks. Our submission has three main components: the Transformer model, corpus cleaning, and right-to-left n-best re-ranking techniques. Through our experiments, we identified two keys for improving accuracy: filtering noisy training sentences and right-to-left re-ranking. We also found that the Transformer model requires more training data than the RNN-based model, and the RNN-based model sometimes achieves better accuracy than the Transformer model when the corpus is small.
Anthology ID:
W18-6421
Volume:
Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Month:
October
Year:
2018
Address:
Belgium, Brussels
Editors:
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
461–466
Language:
URL:
https://aclanthology.org/W18-6421
DOI:
10.18653/v1/W18-6421
Bibkey:
Cite (ACL):
Makoto Morishita, Jun Suzuki, and Masaaki Nagata. 2018. NTT’s Neural Machine Translation Systems for WMT 2018. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 461–466, Belgium, Brussels. Association for Computational Linguistics.
Cite (Informal):
NTT’s Neural Machine Translation Systems for WMT 2018 (Morishita et al., WMT 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-6421.pdf
Data
WMT 2018