This paper describes the University of Tartu's submission to the unsupervised machine translation track of WMT18 news translation shared task. We build several baseline translation systems for both directions of the English-Estonian language pair using monolingual data only; the systems belong to the phrase-based unsupervised machine translation paradigm where we experimented with phrase lengths of up to 3. As a main contribution, we performed a set of standalone experiments with compositional phrase embeddings as a substitute for phrases as individual vocabulary entries. Results show that reasonable n-gram vectors can be obtained by simply summing up individual word vectors which retains or improves the performance of phrase-based unsupervised machine tranlation systems while avoiding limitations of atomic phrase vectors.

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 over-and 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.

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