Octanove Labs’ Japanese-Chinese Open Domain Translation System
Masato Hagiwara · 2020
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.