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

该研究通过使用新型正交结构探针，针对比较语言特征研究并评估了 9 种不同语言中 mBERT 的语境表示所编码的句法和词汇结构信息，并发现对于与英语密切相关的语言，在跨语言嵌入空间中无需进行转换，而对于其他语言，则需要学习分别进行的正交转换。