  Can language models learn grounded representations from text distribution
alone? This question is both central and recurrent in natural language
processing; authors generally agree that grounding requires more than textual
distribution. We propose to experimentally test this claim: if any two words
have different meanings and yet cannot be distinguished from distribution
alone, then grounding is out of the reach of text-based models. To that end, we
present early work on an online game for the collection of human judgments on
the distributional similarity of word pairs in five languages. We further
report early results of our data collection campaign.
