@inproceedings{scherrer-2018-university,
    title = "The {U}niversity of {H}elsinki submissions to the {IWSLT} 2018 low-resource translation task",
    author = "Scherrer, Yves",
    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/add-emnlp-2024-awards/2018.iwslt-1.12/",
    pages = "82--88",
    abstract = "This paper presents the University of Helsinki submissions to the Basque{--}English low-resource translation task. Our primary system is a standard bilingual Transformer system, trained on the available parallel data and various types of synthetic data. We describe the creation of the synthetic datasets, some of which use a pivoting approach, in detail. One of our contrastive submissions is a multilingual model trained on comparable data, but without the synthesized parts. Our bilingual model with synthetic data performed best, obtaining 25.25 BLEU on the test data."
}