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Children learning their first language face multiple problems of induction:how to learn the meanings of words, and how to build meaningful phrases fromthose words according to syntactic rules. We consider how children might solvethese problems efficiently by solving them jointly, via a computational modelthat learns the syntax and semantics of multi-word utterances in a groundedreference game. We select a well-studied empirical case in which children areaware of patterns linking the syntactic and semantic properties of words ---that the properties picked out by base nouns tend to be related to shape, whileprenominal adjectives tend to refer to other properties such as color. We showthat children applying such inductive biases are accurately reflecting thestatistics of child-directed speech, and that inducing similar biases in ourcomputational model captures children's behavior in a classic adjectivelearning experiment. Our model incorporating such biases also demonstrates aclear data efficiency in learning, relative to a baseline model that learnswithout forming syntax-sensitive overhypotheses of word meaning. Thus solving amore complex joint inference problem may make the full problem of languageacquisition easier, not harder.

Word learning and the acquisition of syntactic--semantic overhypotheses