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Multilingual sentence encoders are widely used to transfer NLP models acrosslanguages. The success of this transfer is, however, dependent on the model'sability 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 simplemethod to study how relationships between languages are encoded in twostate-of-the-art multilingual models (i.e. M-BERT and XLM-R). The resultsprovide insight into their information sharing mechanisms and suggest thatlinguistic properties are encoded jointly across typologically-similarlanguages in these models.

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