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

The ADAPT System Description for the IWSLT 2018 Basque to English Translation Task