# Cross-neutralising: Probing for joint encoding of linguistic information in multilingual models

arXiv: https://arxiv.org/abs/2010.12825 · HTML: https://mlsanity.com/paper/2010.12825/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [RuSentEval: Linguistic Source, Encoder Force!](https://mlsanity.com/paper/2103.00573/) (2021)

## Abstract (arXiv)

Multilingual sentence encoders are widely used to transfer NLP models across languages. The success of this transfer is, however, dependent on the model's ability to encode the patterns of cross-lingual similarity and variation. Yet, little is known as to how these models are able to do this. We propose a simple method to study how relationships between languages are encoded in two state-of-the-art multilingual models (i.e. M-BERT and XLM-R). The results provide insight into their information sharing mechanisms and suggest that linguistic properties are encoded jointly across typologically-similar languages in these models.