  In this paper we present the ADAPT system built for the Basque to English Low
Resource MT Evaluation Campaign. Basque is a low-resourced,
morphologically-rich language. This poses a challenge for Neural Machine
Translation models which usually achieve better performance when trained with
large sets of data.
  Accordingly, we used synthetic data to improve the translation quality
produced by a model built using only authentic data. Our proposal uses
back-translated data to: (a) create new sentences, so the system can be trained
with more data; and (b) translate sentences that are close to the test set, so
the model can be fine-tuned to the document to be translated.
