@article{lacunza-etal-2026-acadata,
    title = "{ACAD}ata: Parallel Dataset of Academic Data for Machine Translation",
    author = "Lacunza, I{\~n}aki  and
      Garcia Gilabert, Javier  and
      De Luca Fornaciari, Francesca  and
      Aula-Blasco, Javier  and
      Gonzalez-Agirre, Aitor  and
      Melero, Maite  and
      Villegas, Marta",
    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.671/",
    pages = "8498--8519",
    abstract = "We present ACAData, a high-quality parallel dataset for academic translation, that consists of two subsets: ACAD-Train, which contains approximately 1.5 million human-generated paragraph pairs across 12 languages, and ACAD-Bench, a curated evaluation set of almost 6,000 translations covering 12 directions. To validate its usefulness, we fine-tune two Large Language Models (LLMs) on ACAD-Train and benchmark them on ACAD-Bench against specialized machine-translation systems, general-purpose, open-weight LLMs, and several large-scale proprietary models. Experimental results demonstrate that fine tuning on ACAD-Train leads to improvements in academic translation quality by +6.1 and +12.4 d-BLEU points on average for 7B and 2B models respectively, while also improving long-context translation in a general domain by up to 24.9{\%} when translating out of English. The fine-tuned top-performing model surpasses the best proprietary and open-weight models on the academic translation domain. By releasing ACAD-Train, ACAD-Bench and the fine-tuned models, we provide the community with a valuable resource to advance research in the academic domain and long-context translation."
}