@inproceedings{sanchez-cartagena-2018-prompsit-,
    title = "Prompsit{'}s Submission to the {IWSLT} 2018 Low Resource Machine Translation Task",
    author = "S{\'a}nchez-Cartagena, V{\'i}ctor M.",
    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/fix-sig-urls/2018.iwslt-1.14/",
    pages = "95--103",
    abstract = "This paper presents Prompsit Language Engineering{'}s submission to the IWSLT 2018 Low Resource Machine Translation task. Our submission is based on cross-lingual learning: a multilingual neural machine translation system was created with the sole purpose of improving translation quality on the Basque-to-English language pair. The multilingual system was trained on a combination of in-domain data, pseudo in-domain data obtained via cross-entropy data selection and backtranslated data. We morphologically segmented Basque text with a novel approach that only requires a dictionary such as those used by spell checkers and proved that this segmentation approach outperforms the widespread byte pair encoding strategy for this task."
}