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

arXiv: https://arxiv.org/abs/2510.19217 · HTML: https://mlsanity.com/paper/2510.19217/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 59 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry](https://mlsanity.com/paper/1806.03417/) (2018)
- [XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation](https://mlsanity.com/paper/2104.07412/) (2021)
- [Poincar'e GloVe: Hyperbolic Word Embeddings](https://mlsanity.com/paper/1810.06546/) (2018)
- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [Choosing Transfer Languages for Cross-Lingual Learning](https://mlsanity.com/paper/1905.12688/) (2019)
- [Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languages](https://mlsanity.com/paper/2501.06346/) (2025)
- [Predicting Performance for Natural Language Processing Tasks](https://mlsanity.com/paper/2005.00870/) (2020)
- [Zero-shot cross-lingual transfer language selection using linguistic similarity](https://mlsanity.com/paper/2301.13720/) (2023)
- [Predicting the Performance of Multilingual NLP Models](https://mlsanity.com/paper/2110.08875/) (2021)
- [Phylogeny-Inspired Adaptation of Multilingual Models to New Languages](https://mlsanity.com/paper/2205.09634/) (2022)
- [Dataset Geography: Mapping Language Data to Language Users](https://mlsanity.com/paper/2112.03497/) (2021)
- [URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base](https://mlsanity.com/paper/2409.18472/) (2024)
- [Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models](https://mlsanity.com/paper/2205.06130/) (2022)
- [ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models](https://mlsanity.com/paper/2406.09334/) (2024)
- [Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations](https://mlsanity.com/paper/1910.05479/) (2019)
- [Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity](https://mlsanity.com/paper/2402.02633/) (2024)
- [Identifying the Correlation Between Language Distance and Cross-Lingual Transfer in a Multilingual Representation Space](https://mlsanity.com/paper/2305.02151/) (2023)
- [A Principled Framework for Evaluating on Typologically Diverse Languages](https://mlsanity.com/paper/2407.05022/) (2024)
- [LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization](https://mlsanity.com/paper/2401.06034/) (2024)
- [Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter](https://mlsanity.com/paper/2501.14491/) (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)
- [Latent Tree Models for Hierarchical Topic Detection](https://mlsanity.com/paper/1605.06650/) (2016)
- [Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Features](https://mlsanity.com/paper/2307.05454/) (2023)
- [Geographically-Informed Language Identification](https://mlsanity.com/paper/2403.09892/) (2024)
- [A Survey on Latent Tree Models and Applications](https://mlsanity.com/paper/1402.0577/) (2014)

## Built on by (most cited first, 3 of 3)

- [Simple Additions, Substantial Gains: Expanding Scripts, Languages, and Lineage Coverage in URIEL+](https://mlsanity.com/paper/2510.27183/) (2025)
- [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)

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.