C-3MA: Tartu-Riga-Zurich Translation Systems for WMT17

This paper describes the neural machine translation systems of the University of Latvia, University of Zurich and University of Tartu. We participated in the WMT 2017 shared task on news translation by building systems for two language pairs: English↔German and English↔Latvian. Our systems are based on an attentional encoder-decoder, using BPE subword segmentation. We experimented with backtranslating the monolingual news corpora and filtering out the best translations as additional training data, enforcing named entity translation from a dictionary of parallel named entities, penalizing overand under-translated sentences, and combining output from multiple NMT systems with SMT. The described methods give 0.7 1.8 BLEU point improvements over our baseline systems.