Typology-Guided Adaptation in Multilingual Models

Multilingual models often treat language diversity as a problem of data imbalance, over-looking 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 pro-pose 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 typo-logical structure as a practical and interpretable signal for multilingual model adaptation