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This paper presents a description of CUNI systems submitted to the WMT20 taskon unsupervised and very low-resource supervised machine translation betweenGerman and Upper Sorbian. We experimented with training on synthetic data andpre-training on a related language pair. In the fully unsupervised scenario, weachieved 25.5 and 23.7 BLEU translating from and into Upper Sorbian,respectively. Our low-resource systems relied on transfer learning fromGerman-Czech parallel data and achieved 57.4 BLEU and 56.1 BLEU, which is animprovement of 10 BLEU points over the baseline trained only on the availablesmall German-Upper Sorbian parallel corpus.

CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20