  All natural languages exhibit a distinction between content words (like nouns
and adjectives) and function words (like determiners, auxiliaries,
prepositions). Yet surprisingly little has been said about the emergence of
this universal architectural feature of natural languages. Why have human
languages evolved to exhibit this division of labor between content and
function words? How could such a distinction have emerged in the first place?
This paper takes steps towards answering these questions by showing how the
distinction can emerge through reinforcement learning in agents playing a
signaling game across contexts which contain multiple objects that possess
multiple perceptually salient gradable properties.
