@inproceedings{liu-etal-2025-toward,
    title = "Toward Global {AI} Inclusivity: A Large-Scale Multilingual Terminology Dataset ({GIST})",
    author = "Liu, Jiarui  and
      Ouzzani, Iman  and
      Li, Wenkai  and
      Zhang, Lechen  and
      Ou, Tianyue  and
      Bouamor, Houda  and
      Jin, Zhijing  and
      Diab, Mona T.",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.1148/",
    pages = "22327--22360",
    ISBN = "979-8-89176-256-5",
    abstract = "The field of machine translation has achieved significant advancements, yet domain-specific terminology translation, particularly in AI, remains challenging. This work introduces GIST, a large-scale multilingual AI terminology dataset containing 5K terms extracted from top AI conference papers spanning 2000 to 2023. The terms were translated into Arabic, Chinese, French, Japanese, and Russian using a hybrid framework that combines LLMs for extraction with human expertise for translation. The dataset{'}s quality was benchmarked against existing resources, demonstrating superior translation accuracy through crowdsourced evaluation. GIST was integrated into translation workflows using post-translation refinement methods that required no retraining, where LLM prompting consistently improved BLEU and COMET scores. A web demonstration on the ACL Anthology platform highlights its practical application, showcasing improved accessibility for non-English speakers. We address a critical gap in AI terminology resources and fosters global inclusivity and collaboration in AI research."
}