  The ability to correctly model distinct meanings of a word is crucial for the
effectiveness of semantic representation techniques. However, most existing
evaluation benchmarks for assessing this criterion are tied to sense
inventories (usually WordNet), restricting their usage to a small subset of
knowledge-based representation techniques. The Word-in-Context dataset (WiC)
addresses the dependence on sense inventories by reformulating the standard
disambiguation task as a binary classification problem; but, it is limited to
the English language. We put forward a large multilingual benchmark, XL-WiC,
featuring gold standards in 12 new languages from varied language families and
with different degrees of resource availability, opening room for evaluation
scenarios such as zero-shot cross-lingual transfer. We perform a series of
experiments to determine the reliability of the datasets and to set performance
baselines for several recent contextualized multilingual models. Experimental
results show that even when no tagged instances are available for a target
language, models trained solely on the English data can attain competitive
performance in the task of distinguishing different meanings of a word, even
for distant languages. XL-WiC is available at
https://pilehvar.github.io/xlwic/.
