Cross-Lingual Transfer with Typological Constraints: A Case Study in Low-Resource NLP Article Swipe
Cross-lingual transfer learning has become a cornerstone of multilingual NLP, yet performance disparities persist for low-resource languages, particularly those with typologically divergent features from high-resource source languages. This paper investigates how explicit typological constraints— derived from databases like the World Atlas of Language Structures (WALS) Dryer and Haspelmath [2013]—can guide parameter sharing and alignment in multilingual models. Building on recent work in typologically informed neural architectures Ponti et al. [2020], Bjerva and Augenstein [2021], we propose a novel adapter-based framework that conditions layer-wise transformations on syntactic and morphological features. Our experiments on three low-resource languages (Arapaho, Uyghur, and Tsez) demonstrate that typological guidance reduces negative interference and improves transfer accuracy by up to 12% compared to unconstrained baselines. We further analyze the interplay between feature granularity and model performance, drawing on insights from linguistic typology Bickel and Nichols [2017] and low-resource NLP Joshi et al. [2020].
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31224/4536
- https://engrxiv.org/preprint/download/4536/7882/6502
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https://doi.org/10.31224/4536Digital Object Identifier
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Cross-Lingual Transfer with Typological Constraints: A Case Study in Low-Resource NLPWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
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2025-04-21Full publication date if available
- Authors
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Rosa María Rodríguez JiménezList of authors in order
- Landing page
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https://doi.org/10.31224/4536Publisher landing page
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https://engrxiv.org/preprint/download/4536/7882/6502Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://engrxiv.org/preprint/download/4536/7882/6502Direct OA link when available
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0Total citation count in OpenAlex
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