Octanove Labs’ J apanese- C hinese Open Domain Translation System (2020)

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.

## introduction building a robust, open domain machine translation (mt) system for non-english, asian languages remains a challenge since many mt research efforts have focused mainly on european languages (such as english and german) and/or on particular domains (such as news). this is especially the case when there is lack of high-quality, human-curated parallel corpora and one needs to bootstrap an mt system from noisy parallel data crawled from the web. this is the exact setting of the iwslt 2020 open domain translation challenge @xcite , where the organizers provide large, noisy parallel datasets crawled from the web and the participants build open-domain machine translation systems between japanese (ja) and chinese (zh). the participants are also encouraged to only use the provided datasets to train their models. therefore, the key to building high-quality mt systems seems to be in how to filter and make the most of the provided, noisy datasets. ## parallel corpus filtering our data processing pipeline consists of two main strategies-parallel corpus filtering and backtranslation, which are shown as two main blocks in figure 1 . this section describes the first. ## conclusion this paper describes octanove labs' submission to the iwslt 2020 open domain translation challenge. we combined parallel corpus filtering and back-translation to build a japanese-chinese open domain nmt system. through a series of experiments, we verified that our filtering method is effective in preserving the translation accuracy while greatly reducing the size of parallel data required to train the nmt model. we also found that use of artificially generated parallel data from the remainder of the filtered corpus through back-translation improved the final performance of the system.