Neural Machine Translation between similar South-Slavic languages
Maja Popović, Alberto Poncelas · 2020
This paper describes the ADAPT-DCU machine translation systems built for the WMT 2020 shared task on Similar Language Translation.We explored several set-ups for NMT for Croatian-Slovenian and Serbian-Slovenian language pairs in both translation directions.Our experiments focus on different amounts and types of training data: we first apply basic filtering on the OpenSubtitles training corpora, then we perform additional cleaning of remaining misaligned segments based on character n-gram matching.Finally, we make use of additional monolingual data by creating synthetic parallel data through back-translation.Automatic evaluation shows that multilingual systems with joint Serbian and Croatian data are better than bilingual, as well as that character-based cleaning leads to improved scores while using less data.The results also confirm once more that adding back-translated data further improves the performance, especially when the synthetic data is similar to the desired domain of the development and test set.This, however, might come at a price of prolonged training time, especially for multitarget systems.