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In this paper we describe the CUNI translation system used for theunsupervised news shared task of the ACL 2019 Fourth Conference on MachineTranslation (WMT19). We follow the strategy of Artexte et al. (2018b), creatinga seed phrase-based system where the phrase table is initialized fromcross-lingual embedding mappings trained on monolingual data, followed by aneural machine translation system trained on synthetic parallel data. Thesynthetic corpus was produced from a monolingual corpus by a tuned PBMT modelrefined through iterative back-translation. We further focus on the handling ofnamed entities, i.e. the part of vocabulary where the cross-lingual embeddingmapping suffers most. Our system reaches a BLEU score of 15.3 on theGerman-Czech WMT19 shared task.

CUNI Systems for the Unsupervised News Translation Task in WMT 2019