  Multilingual contextual embeddings, such as multilingual BERT and
XLM-RoBERTa, have proved useful for many multi-lingual tasks. Previous work
probed the cross-linguality of the representations indirectly using zero-shot
transfer learning on morphological and syntactic tasks. We instead investigate
the language-neutrality of multilingual contextual embeddings directly and with
respect to lexical semantics. Our results show that contextual embeddings are
more language-neutral and, in general, more informative than aligned static
word-type embeddings, which are explicitly trained for language neutrality.
Contextual embeddings are still only moderately language-neutral by default, so
we propose two simple methods for achieving stronger language neutrality:
first, by unsupervised centering of the representation for each language and
second, by fitting an explicit projection on small parallel data. Besides, we
show how to reach state-of-the-art accuracy on language identification and
match the performance of statistical methods for word alignment of parallel
sentences without using parallel data.
