# Cross-Linguistic Transfer in Multilingual NLP: The Role of Language Families and Morphology

arXiv: https://arxiv.org/abs/2505.13908 · HTML: https://mlsanity.com/paper/2505.13908/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 3 of 3)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection](https://mlsanity.com/paper/1810.07125/) (2018)

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

- [Tokenization and Morphological Fidelity in Uralic NLP: A Cross-Lingual Evaluation](https://mlsanity.com/paper/2602.04241/) (2026)
- [Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer](https://mlsanity.com/paper/2606.19346/) (2026)
- [A P=aninian Foundation for Indic Language Processing](https://mlsanity.com/paper/2606.24172/) (2026)

## Abstract (arXiv)

Cross-lingual transfer has become a crucial aspect of multilingual NLP, as it allows for models trained on resource-rich languages to be applied to low-resource languages more effectively. Recently massively multilingual pre-trained language models (e.g., mBERT, XLM-R) demonstrate strong zero-shot transfer capabilities[14] [13]. This paper investigates cross-linguistic transfer through the lens of language families and morphology. Investigating how language family proximity and morphological similarity affect performance across NLP tasks. We further discuss our results and how it relates to findings from recent literature. Overall, we compare multilingual model performance and review how linguistic distance metrics correlate with transfer outcomes. We also look into emerging approaches that integrate typological and morphological information into model pre-training to improve transfer to diverse languages[18] [19].