Pretrained Language Models and Backtranslation for English-Basque Biomedical Neural Machine Translation

This paper describes the machine translation systems proposed by the University of Technology Sydney Natural Language Processing (UTS_NLP) team for the WMT20 English-Basque biomedical translation tasks. Due to the limited parallel corpora available, we have proposed to train a BERT-fused NMT model that leverages the use of pretrained language models. Furthermore, we have augmented the training corpus by backtranslating monolingual data. Our experiments show that NMT models in low-resource scenarios can benefit from combining these two training techniques, with improvements of up to 6.16 BLEU percentual points in the case of biomedical abstract translations.

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