@inproceedings{pandya-etal-2025-swahili,
    title = "{S}wahili News Classification: Performance, Challenges, and Explainability Across {ML}, {DL}, and Transformers",
    author = "Pandya, Manas  and
      Sharma, Avinash Kumar  and
      Shukla, Arpit",
    editor = "Lignos, Constantine  and
      Abdulmumin, Idris  and
      Adelani, David",
    booktitle = "Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.africanlp-1.30/",
    pages = "203--209",
    ISBN = "979-8-89176-257-2",
    abstract = "In this paper, we propose a comprehensive framework for the classification of Swahili news articles using a combination of classical machine learning techniques, deep neural networks, and transformer-based models. By balancing two diverse datasets sourced from Harvard Dataverse and Kaggle, our approach addresses the inherent challenges of imbalanced data in low-resource languages. Our experiments demonstrate the effectiveness of the proposed methodology and set the stage for further advances in Swahili natural language processing."
}