It's Not Greek To Mbert: Inducing Word-level Translations From Multilingual BERT

Gonen Hila, Ravfogel Shauli, Elazar Yanai, Goldberg Yoav. Arxiv 2020

[Paper]    
BERT Fine Tuning Model Architecture Pretraining Methods Tools Training Techniques

Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages. We study the word-level translation information embedded in mBERT and present two simple methods that expose remarkable translation capabilities with no fine-tuning. The results suggest that most of this information is encoded in a non-linear way, while some of it can also be recovered with purely linear tools. As part of our analysis, we test the hypothesis that mBERT learns representations which contain both a language-encoding component and an abstract, cross-lingual component, and explicitly identify an empirical language-identity subspace within mBERT representations.

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