  Children learning their first language face multiple problems of induction:
how to learn the meanings of words, and how to build meaningful phrases from
those words according to syntactic rules. We consider how children might solve
these problems efficiently by solving them jointly, via a computational model
that learns the syntax and semantics of multi-word utterances in a grounded
reference game. We select a well-studied empirical case in which children are
aware of patterns linking the syntactic and semantic properties of words ---
that the properties picked out by base nouns tend to be related to shape, while
prenominal adjectives tend to refer to other properties such as color. We show
that children applying such inductive biases are accurately reflecting the
statistics of child-directed speech, and that inducing similar biases in our
computational model captures children's behavior in a classic adjective
learning experiment. Our model incorporating such biases also demonstrates a
clear data efficiency in learning, relative to a baseline model that learns
without forming syntax-sensitive overhypotheses of word meaning. Thus solving a
more complex joint inference problem may make the full problem of language
acquisition easier, not harder.
