@inproceedings{nakashole-2025-typology,
    title = "Typology-Guided Adaptation in Multilingual Models",
    author = "Nakashole, Ndapa",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1059/",
    pages = "21819--21835",
    ISBN = "979-8-89176-251-0",
    abstract = "Multilingual models often treat language diversity as a problem of data imbalance, overlooking structural variation. We introduce the *Morphological Index* (MoI), a typologically grounded metric that quantifies how strongly a language relies on surface morphology for noun classification. Building on MoI, we propose *MoI-MoE*, a Mixture of Experts model that routes inputs based on morphological structure. Evaluated on 10 Bantu languages{---}a large, morphologically rich and underrepresented family{---}MoI-MoE outperforms strong baselines, improving Swahili accuracy by 14 points on noun class recognition while maintaining performance on morphology-rich languages like Zulu. These findings highlight typological structure as a practical and interpretable signal for multilingual model adaptation."
}