Machine Translation in the Covid domain: an English-Irish case study for LoResMT 2021
<|reference_start|>Machine Translation in the Covid domain: an English-Irish case study for LoResMT 2021: 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 highest-performing 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.<|reference_end|>
@article{lankford2024machine,
    title={Machine Translation in the Covid domain: an English-Irish case study for
  LoResMT 2021},
    author={S'eamus Lankford, Haithem Afli and Andy Way},
    journal={Proceedings of the 4th Workshop on Technologies for MT of Low
  Resource Languages (LoResMT2021)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01196},
    primaryClass={cs.CL cs.AI}
}