# Simple Additions, Substantial Gains: Expanding Scripts, Languages, and Lineage Coverage in URIEL+

arXiv: https://arxiv.org/abs/2510.27183 · HTML: https://mlsanity.com/paper/2510.27183/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 46 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Choosing Transfer Languages for Cross-Lingual Learning](https://mlsanity.com/paper/1905.12688/) (2019)
- [UDapter: Language Adaptation for Truly Universal Dependency Parsing](https://mlsanity.com/paper/2004.14327/) (2020)
- [URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base](https://mlsanity.com/paper/2409.18472/) (2024)
- [ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models](https://mlsanity.com/paper/2406.09334/) (2024)
- [Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity](https://mlsanity.com/paper/2402.02633/) (2024)
- [Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment](https://mlsanity.com/paper/2406.19759/) (2024)
- [SIGTYP 2020 Shared Task: Prediction of Typological Features](https://mlsanity.com/paper/2010.08246/) (2020)
- [Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter](https://mlsanity.com/paper/2501.14491/) (2025)
- [Untangling the Influence of Typology, Data and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Tagging](https://mlsanity.com/paper/2503.19979/) (2025)
- [A Reproducibility Study on Quantifying Language Similarity: The Impact of Missing Values in the URIEL Knowledge Base](https://mlsanity.com/paper/2405.11125/) (2024)
- [One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages](https://mlsanity.com/paper/2102.02585/) (2021)
- [Phylogenetic typology](https://mlsanity.com/paper/2103.10198/) (2021)
- [Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+](https://mlsanity.com/paper/2510.19217/) (2025)
- [Unknown Script: Impact of Script on Cross-Lingual Transfer](https://mlsanity.com/paper/2404.18810/) (2024)
- [LangSAMP: Language-Script Aware Multilingual Pretraining](https://mlsanity.com/paper/2409.18199/) (2024)
- [Enhancing Cross-Lingual Transfer through Reversible Transliteration: A Huffman-Based Approach for Low-Resource Languages](https://mlsanity.com/paper/2509.17493/) (2025)

## Built on by (most cited first, 2 of 2)

- [Typological Feature Prediction with Large Language Models: An In-Context Learning Approach](https://mlsanity.com/paper/2609.03775/) (2026)
- [Language Distances are Practical for Equitable Cross-Lingual Transfer](https://mlsanity.com/paper/2609.32331/) (2026)

## Abstract (arXiv)

The URIEL+ linguistic knowledge base supports multilingual research by encoding languages through geographic, genetic, and typological vectors. However, data sparsity (e.g. missing feature types, incomplete language entries, and limited genealogical coverage) remains prevalent. This limits the usefulness of URIEL+ in cross-lingual transfer, particularly for supporting low-resource languages. To address this sparsity, we extend URIEL+ by introducing script vectors to represent writing system properties for 7,488 languages, integrating Glottolog to add 18,710 additional languages, and expanding lineage imputation for 26,449 languages by propagating typological and script features across genealogies. These improvements reduce feature sparsity by 14% for script vectors, increase language coverage by up to 19,015 languages (1,007%), and boost imputation quality metrics by up to 35%. Our benchmark on cross-lingual transfer tasks (oriented around low-resource languages) shows occasionally divergent performance compared to URIEL+, with performance gains up to 6% in certain setups.