Word learning and the acquisition of syntactic–semantic overhypotheses

AbstractChildren 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 tasks efficiently as a joint problem, via a computational model learning the syntax and semantics of utterances in a reference game. We select an 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 statistics of child-directed speech, and that inducing similar biases in our computational model yields a more data-efficient learner which can capture children's generalization behavior. Thus solving a more complex joint inference problem may make the full problem of language acquisition easier, not harder.

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