@inproceedings{poncelas-etal-2018-adapt,
    title = "The {ADAPT} System Description for the {IWSLT} 2018 {B}asque to {E}nglish Translation Task",
    author = "Poncelas, Alberto  and
      Way, Andy  and
      Sarasola, Kepa",
    editor = "Turchi, Marco  and
      Niehues, Jan  and
      Frederico, Marcello",
    booktitle = "Proceedings of the 15th International Conference on Spoken Language Translation",
    month = oct # " 29-30",
    year = "2018",
    address = "Brussels",
    publisher = "International Conference on Spoken Language Translation",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2018.iwslt-1.11/",
    pages = "76--82",
    abstract = "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."
}