@article{tiedemann-luo-2026-opensubtitles2024,
    title = "{O}pen{S}ubtitles2024: A Massively Parallel Dataset of Movie Subtitles for {MT} Development and Evaluation",
    author = "Tiedemann, Joerg  and
      Luo, Hengyu",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.700/",
    pages = "8897--8907",
    abstract = "This paper introduces OpenSubtitles2024, a massively parallel dataset compiled from translated subtitles. The collection includes an extensive collection of aligned training data based on user-contributed subtitles derived from OpenSubtitles.org and a dedicated held-out dataset for development and evaluation of machine translation and multilingual language models. The collection provides an increased language coverage and doubles the size of the previous edition. Furthermore, a careful procedure was applied to reserve a subset of the most recent subtitles for system development and evaluation. The collection covers 92 languages and language variants, aligned in over 3,000 bitexts containing 40 billion tokens in 7.7 million subtitle files. The test set comprises 2,022 language pairs. In addition, we also provide a multi-parallel test set that refers to a subset of the held-out data with synchronized alignments across 40 languages and 15 subtitles."
}