  Multilingual pretrained language models (MPLMs) exhibit multilinguality and
are well suited for transfer across languages. Most MPLMs are trained in an
unsupervised fashion and the relationship between their objective and
multilinguality is unclear. More specifically, the question whether MPLM
representations are language-agnostic or they simply interleave well with
learned task prediction heads arises. In this work, we locate language-specific
information in MPLMs and identify its dimensionality and the layers where this
information occurs. We show that language-specific information is scattered
across many dimensions, which can be projected into a linear subspace. Our
study contributes to a better understanding of MPLM representations, going
beyond treating them as unanalyzable blobs of information.
