@inproceedings{ito-etal-2026-context,
    title = "Context-Driven and Reference-Guided Data Augmentation for Subtitle Translation",
    author = "Ito, Hitoshi  and
      Shirai, Naoto  and
      Kinugawa, Kazutaka  and
      Mino, Hideya  and
      Endo, Rei  and
      Kawai, Yoshihiko",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.2059/",
    pages = "41381--41394",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) have demonstrated strong performance in translation tasks. Subtitle translation presents unique challenges, such as preserving the original work{'}s worldview and the distinctive speaking styles of its characters. Achieving high-quality translations that reflect these stylistic nuances typically requires bilingual data for a specific movie, which is often scarce or unavailable. Thus, we propose a data augmentation method that uses LLMs to improve translation performance for specific movies, even when only a few hundred bilingual sentence pairs are available. The method expands source-side data by rewriting original subtitles using information that can be extracted from the context, such as character profiles and scene descriptions, to maintain the tone and thematic consistency of the movie. For translation, the augmented sentences are aligned with manually translated originals using structural similarity, which enables style-preserving bilingual data generation via one-shot learning. Experimental results show that data augmented using the proposed method effectively improves BLEU scores for film subtitle translation, and achieves superior stylistic quality in human evaluation."
}