@inproceedings{lee-etal-2025-team,
    title = "Team {ACK} at {S}em{E}val-2025 Task 2: Beyond Word-for-Word Machine Translation for {E}nglish-{K}orean Pairs",
    author = "Lee, Daniel  and
      Sharma, Harsh  and
      Han, Jieun  and
      Jeong, Sunny  and
      Oh, Alice  and
      Shwartz, Vered",
    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-1.309/",
    pages = "2376--2388",
    ISBN = "979-8-89176-273-2",
    abstract = "Translating knowledge-intensive and entity-rich text between English and Korean requires transcreation to preserve language-specific and cultural nuances beyond literal, phonetic or word-for-word conversion. We evaluate 13 models (LLMs and MT systems) using automatic metrics and human assessment by bilingual annotators. Our findings show LLMs outperform traditional MT systems but struggle with entity translation requiring cultural adaptation. By constructing an error taxonomy, we identify incorrect responses and entity name errors as key issues, with performance varying by entity type and popularity level. This work exposes gaps in automatic evaluation metrics and hope to enable future work in completing culturally-nuanced machine translation."
}