Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens.
Published in Findings of the Association for Computational Linguistics: ACL 2026, pages 6632–6655 ©2026 Association for Computational Linguistics., 2026
Recommended citation: Hellina Hailu Nigatu, Bethelhem Yemane Mamo, Bontu Fufa Balcha, **Debora Taye**, Elbethel Daniel Zewdie, Ikram Behiru Nesiru, Jitu Ewnetu Hailu, Senait Mengesha Yayo (2026). "Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens."Findings of the Association for Computational Linguistics: ACL 2026, pages 6632–6655 ©2026 Association for Computational Linguistics. The paper investigates the quality and gender representation of machine translation datasets for three Ethiopian languages: Afan Oromo, Amharic, and Tigrinya. Using automated analyses and human evaluation.
Download Paper