@inproceedings{jones-mitkov-2025-evaluating,
    title = "Evaluating the Performance of Transformers in Translating Low-Resource Languages through {A}kkadian",
    author = "Jones, Daniel A.  and
      Mitkov, Ruslan",
    editor = "Picazo-Izquierdo, Alicia  and
      Estevanell-Valladares, Ernesto Luis  and
      Mitkov, Ruslan  and
      Guillena, Rafael Mu{\~n}oz  and
      Cerd{\'a}, Ra{\'u}l Garc{\'i}a",
    booktitle = "Proceedings of the First Workshop on Comparative Performance Evaluation: From Rules to Language Models",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.r2lm-1.5/",
    pages = "39--47",
    abstract = "In this paper, we evaluate the performance of various fine-tuned, transformer-based models in translating Akkadian into English. Using annotated Akkadian data, we seek to establish potential considerations when developing models for other low-resource languages, which do not yet have as robust data. The results of this study show the potency, but also cost inefficiency, of Large Language Models compared to smaller Neural Machine Translation models. Significant evidence was also found demonstrating the importance of fine-tuning machine translation models from related languages."
}