@inproceedings{oncevay-etal-2025-translating,
    title = "Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education",
    author = "Oncevay, Arturo  and
      Kochkina, Elena  and
      Ramani, Keshav  and
      Aguda, Toyin  and
      Kaur, Simerjot  and
      Smiley, Charese",
    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.1774/",
    pages = "35018--35032",
    ISBN = "979-8-89176-332-6",
    abstract = "Domain-specific multilingual terminology is essential for accurate machine translation (MT) and cross-lingual NLP applications. We present a gold-standard terminology resource for the tax and financial education domains, built from curated governmental publications and covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. Using this resource, we assess various MT systems and LLMs on translation quality and term accuracy. We annotate over 3,000 terms for domain-specificity, facilitating a comparison between domain-specific and general term translations, and observe models' challenges with specialized tax terms. We also analyze the case of terminology-aided translation, and the LLMs' performance in extracting the translated term given the context. Our results highlight model limitations and the value of high-quality terminologies for advancing MT research in specialized contexts."
}