@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://aclanthology.org/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."
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<title>Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026)</title>
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<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.</abstract>
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%0 Conference Proceedings
%T Evaluating Native-Speaker Preferences on Machine Translation and Post-Edits for Five African Languages
%A El Oirghi, Hiba
%A Gwadabe, Tajuddeen
%A Carpuat, Marine
%Y Chimoto, Everlyn Asiko
%Y Lignos, Constantine
%Y Muhammad, Shamsuddeen
%Y Abdulmumin, Idris
%Y Siro, Clemencia
%Y Adelani, David Ifeoluwa
%S Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-364-7
%F el-oirghi-etal-2026-evaluating
%X 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.
%U https://aclanthology.org/2026.africanlp-main.15/
%P 163-170
Markdown (Informal)
[Evaluating Native-Speaker Preferences on Machine Translation and Post-Edits for Five African Languages](https://aclanthology.org/2026.africanlp-main.15/) (El Oirghi et al., AfricaNLP 2026)
ACL