Evaluating Native-Speaker Preferences on Machine Translation and Post-Edits for Five African Languages

Hiba El Oirghi, Tajuddeen Gwadabe, Marine Carpuat

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.

Submission Number: 28