@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://aclanthology.org/2025.acl-long.1059/",
doi = "10.18653/v1/2025.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."
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%0 Conference Proceedings
%T Typology-Guided Adaptation in Multilingual Models
%A Nakashole, Ndapa
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F nakashole-2025-typology
%X 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.
%R 10.18653/v1/2025.acl-long.1059
%U https://aclanthology.org/2025.acl-long.1059/
%U https://doi.org/10.18653/v1/2025.acl-long.1059
%P 21819-21835
Markdown (Informal)
[Typology-Guided Adaptation in Multilingual Models](https://aclanthology.org/2025.acl-long.1059/) (Nakashole, ACL 2025)
ACL
- Ndapa Nakashole. 2025. Typology-Guided Adaptation in Multilingual Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21819–21835, Vienna, Austria. Association for Computational Linguistics.