  In this paper we describe the CUNI translation system used for the
unsupervised news shared task of the ACL 2019 Fourth Conference on Machine
Translation (WMT19). We follow the strategy of Artexte et al. (2018b), creating
a seed phrase-based system where the phrase table is initialized from
cross-lingual embedding mappings trained on monolingual data, followed by a
neural machine translation system trained on synthetic parallel data. The
synthetic corpus was produced from a monolingual corpus by a tuned PBMT model
refined through iterative back-translation. We further focus on the handling of
named entities, i.e. the part of vocabulary where the cross-lingual embedding
mapping suffers most. Our system reaches a BLEU score of 15.3 on the
German-Czech WMT19 shared task.
