Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+

• URL: http://arxiv.org/abs/2510.19217v1
• Date: Wed, 22 Oct 2025 03:59:19 GMT
• Title: Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+
• Authors: York Hay Ng, Aditya Khan, Xiang Lu, Matteo Salloum, Michael Zhou, Phuong H. Hoang, A. Seza Doğruöz, En-Shiun Annie Lee,
• Abstract summary: We introduce a framework for type-matched language distances.<n>We propose novel, structure-aware representations for each distance type.<n>We unify these signals into a robust, task-agnostic composite distance.
• Score: 4.262015666551064
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Existing linguistic knowledge bases such as URIEL+ provide valuable geographic, genetic and typological distances for cross-lingual transfer but suffer from two key limitations. One, their one-size-fits-all vector representations are ill-suited to the diverse structures of linguistic data, and two, they lack a principled method for aggregating these signals into a single, comprehensive score. In this paper, we address these gaps by introducing a framework for type-matched language distances. We propose novel, structure-aware representations for each distance type: speaker-weighted distributions for geography, hyperbolic embeddings for genealogy, and a latent variables model for typology. We unify these signals into a robust, task-agnostic composite distance. In selecting transfer languages, our representations and composite distances consistently improve performance across a wide range of NLP tasks, providing a more principled and effective toolkit for multilingual research.

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