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