# Understanding Cross-Lingual Syntactic Transfer in Multilingual Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2003.14056 · HTML: https://mlsanity.com/paper/2003.14056/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs](https://mlsanity.com/paper/2504.02768/) (2025)
- [Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languages](https://mlsanity.com/paper/2501.06346/) (2025)
- [Subword Mapping and Anchoring across Languages](https://mlsanity.com/paper/2109.04556/) (2021)
- [Exploring syntactic information in sentence embeddings through multilingual subject-verb agreement](https://mlsanity.com/paper/2409.06567/) (2024)
- [Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation](https://mlsanity.com/paper/2201.01845/) (2022)
- [A Study of Crosslinguistic Influence in Language Models](https://mlsanity.com/paper/2601.21587/) (2026)
- [The Impact of Vocabulary Overlaps on Knowledge Transfer in Multilingual Machine Translation](https://mlsanity.com/paper/2605.04196/) (2026)

## Abstract (arXiv)

It is now established that modern neural language models can be successfully trained on multiple languages simultaneously without changes to the underlying architecture. But what kind of knowledge is really shared among languages within these models? Does multilingual training mostly lead to an alignment of the lexical representation spaces or does it also enable the sharing of purely grammatical knowledge? In this paper we dissect different forms of cross-lingual transfer and look for its most determining factors, using a variety of models and probing tasks. We find that exposing our LMs to a related language does not always increase grammatical knowledge in the target language, and that optimal conditions for lexical-semantic transfer may not be optimal for syntactic transfer.