@inproceedings{poncelas-htun-2025-sakura,
    title = "Sakura at {S}em{E}val-2025 Task 2: Enhancing Named Entity Translation with Fine-Tuning and Preference Optimization",
    author = "Poncelas, Alberto  and
      Htun, Ohnmar",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.108/",
    pages = "791--796",
    ISBN = "979-8-89176-273-2",
    abstract = "Translating name entities can be challenging, as it often requires real-world knowledge rather than just performing a literal translation. The shared task ``Entity-Aware Machine Translation'' in SemEval-2025 encourages participants to build machine translation models that can effectively handle the translation of complex named entities.In this paper, we propose two methods to improve the accuracy of name entity translation from English to Japanese. One approach involves fine-tuning the model on entries, or lists of entries, of the dictionary. The second technique focuses on preference optimization, guiding the model on which translation it should generate."
}