Year: 2025
Type: conference-paper
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.
Author: Ndapa Nakashole
Institution: University of California San Diego
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412944885