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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Neural Machine Translation between similar South-Slavic languages
%A Popović, Maja
%A Poncelas, Alberto
%S Proceedings of the Fifth Conference on Machine Translation
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F popovic-poncelas-2020-neural
%X 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.
%U https://aclanthology.org/2020.wmt-1.51
%P 430-436

Markdown (Informal)

[Neural Machine Translation between similar South-Slavic languages](https://aclanthology.org/2020.wmt-1.51) (Popović & Poncelas, WMT 2020)

• Neural Machine Translation between similar South-Slavic languages (Popović & Poncelas, WMT 2020)

ACL

• Maja Popović and Alberto Poncelas. 2020. Neural Machine Translation between similar South-Slavic languages. In Proceedings of the Fifth Conference on Machine Translation, pages 430–436, Online. Association for Computational Linguistics.