Abstract
We participated in the WMT 2018 shared news translation task in three language pairs: English-Estonian, English-Finnish, and English-Czech. Our main focus was the lowresource language pair of Estonian and English for which we utilized Finnish parallel data in a simple method.
We first train a "parent model" for the high-resource language pair followed by adaptation on the related lowresource language pair. This approach brings a substantial performance boost over the baseline system trained only on Estonian-English parallel data.
Our systems are based on the Transformer architecture. For the English to Czech translation, we have evaluated our last year models of hybrid phrase-based approach and neural machine translation mainly for comparison purposes.