Octanove Labs’ Japanese-Chinese Open Domain Translation System
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
- URL:
- https://www.aclweb.org/anthology/2020.iwslt-1.20
- DOI:
- PDF:
- https://www.aclweb.org/anthology/2020.iwslt-1.20.pdf
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