Papers by Bethelhem Yemane Mamo
Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens (2026.findings-acl)
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Hellina Hailu Nigatu, Bethelhem Yemane Mamo, Bontu Fufa Balcha, Debora Taye Tesfaye, Elbethel Daniel Zewdie, Ikram Behiru Nesiru, Jitu Ewnetu Hailu, Senait Mengesha Yayo
| Challenge: | afan oromo, amharic, and tigrinya are low-resourced languages . they are used for training, benchmarks, news, health, and sports . afono o'mara: quantity does not guarantee quality of MT datasets . |
| Approach: | They investigate the quality of machine translation datasets for three low-resourced languages . they found a large skew towards the male gender in the datasets . |
| Outcome: | The results show that training data has large representation of political and religious text, but benchmark datasets focus on news, health, and sports. |