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.