  Recent work has shown the surprising ability of multi-lingual BERT to serve
as a zero-shot cross-lingual transfer model for a number of language processing
tasks. We combine this finding with a similarly-recently proposal on
sentence-level relevance modeling for document retrieval to demonstrate the
ability of multi-lingual BERT to transfer models of relevance across languages.
Experiments on test collections in five different languages from diverse
language families (Chinese, Arabic, French, Hindi, and Bengali) show that
models trained with English data improve ranking quality, without any special
processing, both for (non-English) mono-lingual retrieval as well as
cross-lingual retrieval.
