@inproceedings{muhammad-etal-2025-hausanlp,
    title = "{H}ausa{NLP}: Current Status, Challenges and Future Directions for {H}ausa Natural Language Processing",
    author = "Muhammad, Shamsuddeen Hassan  and
      Ahmad, Ibrahim Said  and
      Abdulmumin, Idris  and
      Lawan, Falalu Ibrahim  and
      Imam, Sukairaj Hafiz  and
      Aliyu, Yusuf  and
      Sani, Sani Abdullahi  and
      Umar, Ali Usman  and
      Gwadabe, Tajuddeen  and
      Church, Kenneth  and
      Marivate, Vukosi",
    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-1.27/",
    pages = "176--191",
    ISBN = "979-8-89176-257-2",
    abstract = "Hausa Natural Language Processing (NLP) has gained increasing attention in recent years, yet remains understudied as a low-resource language despite having over 120 million first-language (L1) and 80 million second-language (L2) speakers worldwide. While significant advances have been made in high-resource languages, Hausa NLP faces persistent challenges including limited open-source datasets and inadequate model representation. This paper presents an overview of the current state of Hausa NLP, systematically examining existing resources, research contributions, and gaps across fundamental NLP tasks: text classification, machine translation, named entity recognition, speech recognition, and question answering. We introduce HausaNLP, a curated catalog that aggregates datasets, tools, and research works to enhance accessibility and drive further development. Furthermore, we discuss challenges in integrating Hausa into large language models (LLMs), addressing issues of suboptimal tokenization, and dialectal variation. Finally, we propose strategic research directions emphasizing dataset expansion, improved language modeling approaches, and strengthened community collaboration to advance Hausa NLP. Our work provides both a foundation for accelerating Hausa NLP progress and valuable insights for broader multilingual NLP research."
}