@inproceedings{el-oirghi-etal-2026-evaluating,
    title = "Evaluating Native-Speaker Preferences on Machine Translation and Post-Edits for Five {A}frican Languages",
    author = "El Oirghi, Hiba  and
      Gwadabe, Tajuddeen  and
      Carpuat, Marine",
    editor = "Chimoto, Everlyn Asiko  and
      Lignos, Constantine  and
      Muhammad, Shamsuddeen  and
      Abdulmumin, Idris  and
      Siro, Clemencia  and
      Adelani, David Ifeoluwa",
    booktitle = "Proceedings of the 7th Workshop on {A}frican Natural Language Processing ({A}frica{NLP} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.africanlp-main.15/",
    pages = "163--170",
    ISBN = "979-8-89176-364-7",
    abstract = "Wikipedia editors undertake the task of editing machine translation (MT) outputs in various languages to disseminate multilingual knowledge from English. But are editors doing more than just translating or fixing MT output? To answer this broad question, we constructed a dataset of 4,335 fine-grained annotated parallel pairs of MT translations and human post-edit (HE) translations for five low-resource African languages: Hausa, Igbo, Swahili, Yoruba, and Zulu. We report on our data selection and annotation methodologies as well as findings from the annotated dataset, the most surprising of which is that annotators mostly preferred the MT translations over their HE counterparts for three out of five languages. We analyze the nature of these ``fluency breaking'' edits and provide recommendations for the MT post-editing workflows in the Wikipedia domain and beyond."
}