@inproceedings{lankford-etal-2021-machine,
    title = "Machine Translation in the Covid domain: an {E}nglish-{I}rish case study for {L}o{R}es{MT} 2021",
    author = "Lankford, Seamus  and
      Afli, Haithem  and
      Way, Andy",
    editor = "Ortega, John  and
      Ojha, Atul Kr.  and
      Kann, Katharina  and
      Liu, Chao-Hong",
    booktitle = "Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)",
    month = aug,
    year = "2021",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.mtsummit-loresmt.15/",
    pages = "144--150",
    abstract = "Translation models for the specific domain of translating Covid data from English to Irish were developed for the LoResMT 2021 shared task. Domain adaptation techniques, using a Covid-adapted generic 55k corpus from the Directorate General of Translation, were applied. Fine-tuning, mixed fine-tuning and combined dataset approaches were compared with models trained on an extended in-domain dataset. As part of this study, an English-Irish dataset of Covid related data, from the Health and Education domains, was developed. The highestperforming model used a Transformer architecture trained with an extended in-domain Covid dataset. In the context of this study, we have demonstrated that extending an 8k in-domain baseline dataset by just 5k lines improved the BLEU score by 27 points."
}