---
title: "Modality Matching Matters — paper by Ng et al. (2026)"
description: "The authors argue that each modality needs a representation that fits its own structure: Geography: each language becomes a… 58 references."
authors: ["York Hay Ng", "Aditya Khan", "Xiang Lu", "Matteo Salloum", "Michael Zhou", "Phuong Hanh Hoang", "A. Seza Doğruöz", "En-Shiun Annie Lee"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)"
source: "https://aclanthology.org/2026.eacl-srw.8/"
doi: "10.18653/v1/2026.eacl-srw.8"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ng-2026-modality-matching-matters-calibrating-language/"
---
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# Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+

**Authors:** [York Hay Ng](https://md.research.elamur.ai/free/en/authors/york-hay-ng/), [Aditya Khan](https://md.research.elamur.ai/free/en/authors/aditya-khan/), [Xiang Lu](https://md.research.elamur.ai/free/en/authors/xiang-lu/), Matteo Salloum, Michael Zhou, Phuong Hanh Hoang, [A. Seza Doğruöz](https://md.research.elamur.ai/free/en/authors/a-seza-dogruoz/) and [En-Shiun Annie Lee](https://md.research.elamur.ai/free/en/authors/en-shiun-annie-lee/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-srw.8/) · [PDF](https://aclanthology.org/2026.eacl-srw.8.pdf) · [DOI 10.18653/v1/2026.eacl-srw.8](https://doi.org/10.18653/v1/2026.eacl-srw.8) · [OpenAlex](https://openalex.org/W7140125489)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
URIEL+ provides geographic, genetic and typological distances for over 7,000 languages. It stores all three as high-dimensional Euclidean vectors compared by angular distance, and it has no principled way to combine them into one number. The authors argue that each modality needs a representation that fits its own structure:

- **Geography:** each language becomes a distribution of speakers across countries, built from Ethnologue counts, and languages are compared with the Wasserstein-1 distance.
- **Genealogy:** the whole Glottolog tree, including internal family nodes, is embedded in hyperbolic space.
- **Typology:** correlated URIEL+ features are grouped by a latent tree model into compact latent "islands".

A weighted average of the three gives a single, task-agnostic composite distance. The authors evaluated these distances through source-language selection with LangRank on several zero-shot transfer benchmarks. The matched representations gave statistically significant gains over URIEL+ when the modality was relevant to the task. The composite score outperformed URIEL+ on most tasks. The study shows that transfer prediction depends on how languages are represented, not only on which distance is chosen.

## Key points
- The baseline problems in URIEL+ are concrete:
  - Geography uses one Glottolog coordinate per language, measured against 299 fixed reference points.
  - Genetic vectors have over 3,700 dimensions and are about 99.85% zeros.
  - Roughly 87% of typological values are missing before imputation.
- The authors compared Poincaré-ball and hyperboloid (Lorentz) models for the genealogy embeddings. They describe this as the first direct hyperbolic embedding of the full Glottolog hierarchy.
- Which single modality helps most varies by task. Results also differed across benchmarks within the same task type, for example XNLI versus SIB200.
- The composite distance is a strong general baseline, but it gives up some task-specific performance in exchange for broad applicability.
- Code is available at github.com/Swithord/urielplus-modality-matters, along with the hyperbolic genetic embeddings and the Latent Island typology representations. The speaker distributions are not fully released because Ethnologue data is proprietary; only a limited subset is shared.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: York Hay Ng, Aditya Khan, Xiang Lu, Matteo Salloum, Michael Zhou, Phuong Hanh Hoang, A. Seza Doğruöz and En-Shiun Annie Lee. 2026. Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ng2026modality,
  title = {{Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+}},
  author = {York Hay Ng, Aditya Khan, Xiang Lu, Matteo Salloum, Michael Zhou, Phuong Hanh Hoang, A. Seza Doğruöz and En-Shiun Annie Lee},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-srw.8},
  url = {https://doi.org/10.18653/v1/2026.eacl-srw.8}
}
```

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*Updated 2 November 2026*