@inproceedings{ng-etal-2026-modality,
    title = "Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in {URIEL}+",
    author = {Ng, York Hay  and
      Khan, Aditya  and
      Lu, Xiang  and
      Salloum, Matteo  and
      Zhou, Michael  and
      Hoang, Phuong Hanh  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Lee, En-Shiun Annie},
    editor = "Baez Santamaria, Selene  and
      Somayajula, Sai Ashish  and
      Yamaguchi, Atsuki",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-srw.8/",
    pages = "110--130",
    ISBN = "979-8-89176-383-8",
    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. 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 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. Across multiple zero-shot transfer benchmarks, we demonstrate that our representations significantly improve transfer performance when the distance type is relevant to the task, while our composite distance yields gains in most tasks."
}