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.

本文研究了多语种上下文嵌入的语言中性和词汇语义，提出了两种实现更强语言中性的方法，并展示了在不使用平行数据的情况下如何达到语言识别的最新准确率以及匹配平行句子的词对齐统计方法的性能。