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State-of-the-art contextual embeddings are obtained from large languagemodels available only for a few languages. For others, we need to learnrepresentations using a multilingual model. There is an ongoing debate onwhether multilingual embeddings can be aligned in a space shared across manylanguages. The novel Orthogonal Structural Probe (Limisiewicz and Mare\v{c}ek,2021) allows us to answer this question for specific linguistic features andlearn a projection based only on mono-lingual annotated datasets. We evaluatesyntactic (UD) and lexical (WordNet) structural information encoded inmBERT'scontextual representations for nine diverse languages. We observe that forlanguages closely related to English, no transformation is needed. Theevaluated information is encoded in a shared cross-lingual embedding space. Forother languages, it is beneficial to apply orthogonal transformation learnedseparately for each language. We successfully apply our findings to zero-shotand few-shot cross-lingual parsing.

Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes