@inproceedings{alabi-etal-2025-afridoc,
    title = "{AFRIDOC}-{MT}: Document-level {MT} Corpus for {A}frican Languages",
    author = "Alabi, Jesujoba Oluwadara  and
      Azime, Israel Abebe  and
      Zhang, Miaoran  and
      Espa{\~n}a-Bonet, Cristina  and
      Bawden, Rachel  and
      Zhu, Dawei  and
      Adelani, David Ifeoluwa  and
      Odoje, Clement Oyeleke  and
      Akinade, Idris  and
      Maab, Iffat  and
      David, Davis  and
      Muhammad, Shamsuddeen Hassan  and
      Putini, Neo  and
      Ademuyiwa, David O.  and
      Caines, Andrew  and
      Klakow, Dietrich",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1413/",
    pages = "27758--27794",
    ISBN = "979-8-89176-332-6",
    abstract = "This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yor{\`u}b{\'a}, and Zulu. The dataset comprises 334 health and 271 information technology news documents, all human-translated from English to these languages. We conduct document-level translation benchmark experiments by evaluating the ability of neural machine translation (NMT) models and large language models (LLMs) to translate between English and these languages, at both the sentence and pseudo-document levels, the outputs being realigned to form complete documents for evaluation. Our results indicate that NLLB-200 achieves the best average performance among the standard NMT models, while GPT-4o outperforms general-purpose LLMs. Fine-tuning selected models leads to substantial performance gains, but models trained on sentences struggle to generalize effectively to longer documents. Furthermore, our analysis reveals that some LLMs exhibit issues such as under-generation, over-generation, repetition of words and phrases, and off-target translations, specifically for translation into African languages."
}