AI RESEARCH
Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+
arXiv CS.CL
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ArXi:2510.19217v3 Announce Type: replace Existing linguistic knowledge bases such as URIEL+ provide valuable geographic, genetic and typological distances for cross-lingual transfer but suffer from two key limitations. First, their one-size-fits-all vector representations are ill-suited to the diverse structures of linguistic data. Second, they lack a principled method for aggregating these signals into a single, comprehensive score. In this paper, we address these gaps by