  Languages differ in terms of the absence or presence of gender features, the
number of gender classes and whether and where gender features are explicitly
marked. These cross-linguistic differences can lead to ambiguities that are
difficult to resolve, especially for sentence-level MT systems. The
identification of ambiguity and its subsequent resolution is a challenging task
for which currently there aren't any specific resources or challenge sets
available. In this paper, we introduce gENder-IT, an English--Italian challenge
set focusing on the resolution of natural gender phenomena by providing
word-level gender tags on the English source side and multiple gender
alternative translations, where needed, on the Italian target side.
