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

• URL: http://arxiv.org/abs/2010.12825v2
• Date: Sat, 13 Mar 2021 16:21:09 GMT
• Title: Cross-neutralising: Probing for joint encoding of linguistic information in multilingual models
• Authors: Rochelle Choenni, Ekaterina Shutova
• Abstract summary: We study how relationships between languages are encoded in two state-of-the-art multilingual models. The results suggest that linguistic properties are encoded jointly across typologically-similar languages.
• Score: 17.404220737977738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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