@inproceedings{nigatu-etal-2025-viability,
    title = "Viability of Machine Translation for Healthcare in Low-Resourced Languages",
    author = "Nigatu, Hellina Hailu  and
      Mehandru, Nikita  and
      Abadi, Negasi Haile  and
      Gebremeskel, Blen  and
      Alaa, Ahmed  and
      Choudhury, Monojit",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.535/",
    pages = "10595--10609",
    ISBN = "979-8-89176-332-6",
    abstract = "Machine Translation errors in high-stakes settings like healthcare pose unique risks that could lead to clinical harm. The challenges are even more pronounced for low-resourced languages where human translators are scarce and MT tools perform poorly. In this work, we provide a taxonomy of Machine Translation errors for the healthcare domain using a publicly available MT system. Preparing an evaluation dataset from pre-existing medical datasets, we conduct our study focusing on two low-resourced languages: Amharic and Tigrinya. Based on our error analysis and findings from prior work, we test two pre-translation interventions{--}namely, paraphrasing the source sentence and pivoting with a related language{--} for their effectiveness in reducing clinical risk. We find that MT errors for healthcare most commonly happen when the source sentence includes medical terminology and procedure descriptions, synonyms, figurative language, and word order differences. We find that pre-translation interventions are not effective in reducing clinical risk if the base translation model performs poorly. Based on our findings, we provide recommendations for improving MT for healthcare."
}