@inproceedings{nigatu-etal-2026-evaluating,
    title = "Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens",
    author = "Nigatu, Hellina Hailu  and
      Mamo, Bethelhem Yemane  and
      Balcha, Bontu Fufa  and
      Tesfaye, Debora Taye  and
      Zewdie, Elbethel Daniel  and
      Nesiru, Ikram Behiru  and
      Hailu, Jitu Ewnetu  and
      Yayo, Senait Mengesha",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.330/",
    pages = "6632--6655",
    ISBN = "979-8-89176-395-1",
    abstract = "As low-resourced languages are increasingly incorporated into NLP research, there is an emphasis on collecting large-scale datasets. But in prioritizing quantity over quality, we risk 1) building language technologies that perform poorly for these languages and 2) producing harmful content that perpetuates societal biases. In this paper, we investigate the quality of Machine Translation (MT) datasets for three low-resourced languages{--}Afan Oromo, Amharic, and Tigrinya, with a focus on the gender representation in the datasets. Our findings demonstrate that while training data has a large representation of political and religious domain text, benchmark datasets are focused on news, health, and sports. We also found a large skew towards the male gender{--}in names of persons, the grammatical gender of verbs, and in stereotypical depictions in the datasets. Further, we found harmful and toxic depictions against women, which were more prominent for the language with the largest amount of data, underscoring that quantity does not guarantee quality. We hope that our work inspires further inquiry into the datasets collected for low-resourced languages and prompts early mitigation of harmful content. WARNING: This paper contains discussion of NSFW content that some may find disturbing."
}