Typology-Guided Adaptation for African NLP
Domain:
natural language processingRecord type:
modelpaper
Creator:
AssNak
Publisher:
Und
Host:
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.
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