  While recent work on multilingual language models has demonstrated their
capacity for cross-lingual zero-shot transfer on downstream tasks, there is a
lack of consensus in the community as to what shared properties between
languages enable such transfer. Analyses involving pairs of natural languages
are often inconclusive and contradictory since languages simultaneously differ
in many linguistic aspects. In this paper, we perform a large-scale empirical
study to isolate the effects of various linguistic properties by measuring
zero-shot transfer between four diverse natural languages and their
counterparts constructed by modifying aspects such as the script, word order,
and syntax. Among other things, our experiments show that the absence of
sub-word overlap significantly affects zero-shot transfer when languages differ
in their word order, and there is a strong correlation between transfer
performance and word embedding alignment between languages (e.g., R=0.94 on the
task of NLI). Our results call for focus in multilingual models on explicitly
improving word embedding alignment between languages rather than relying on its
implicit emergence.
