Linguistic feature datasets such as URIEL+ are valuable for modelling cross-lingual relationships, but their high dimensionality and sparsity, especially for low-resource languages, limit the effectiveness of distance metrics. We propose a pipeline to optimize the URIEL+ typological feature space by combining feature selection and imputation, producing compact yet interpretable typological representations. We evaluate these feature subsets on linguistic distance alignment and downstream tasks, demonstrating that reduced-size representations of language typology can yield more informative distance metrics and improve performance in multilingual NLP applications.
@article{arxiv.2509.20129,
title = {Less is More: The Effectiveness of Compact Typological Language Representations},
author = {York Hay Ng and Phuong Hanh Hoang and En-Shiun Annie Lee},
journal= {arXiv preprint arXiv:2509.20129},
year = {2025}
}