Octanove Labs’ Japanese-Chinese Open Domain Translation System

Masato Hagiwara


Abstract
This paper describes Octanove Labs’ submission to the IWSLT 2020 open domain translation challenge. In order to build a high-quality Japanese-Chinese neural machine translation (NMT) system, we use a combination of 1) parallel corpus filtering and 2) back-translation. We have shown that, by using heuristic rules and learned classifiers, the size of the parallel data can be reduced by 70% to 90% without much impact on the final MT performance. We have also shown that including the artificially generated parallel data through back-translation further boosts the metric by 17% to 27%, while self-training contributes little. Aside from a small number of parallel sentences annotated for filtering, no external resources have been used to build our system.
Anthology ID:
2020.iwslt-1.20
Volume:
Proceedings of the 17th International Conference on Spoken Language Translation
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | IWSLT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
166–171
Language:
URL:
https://www.aclweb.org/anthology/2020.iwslt-1.20
DOI:
10.18653/v1/2020.iwslt-1.20
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.iwslt-1.20.pdf