A Computational Model for Early Argument Structure Acquisition

How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behaviour over the course of learning similar to those in children. Early learning of verb argument structure is an area of language acquisition that provides an interesting testbed for such approaches due to the complexity of verb usages. A range of linguistic factors interact in determining the felicitous use of a verb in various constructions—associations between syntactic forms and properties of meaning, that form the basis for a number of linguistic and psycholinguistic theories of language. We present a computational model for the representation, acquisition, and use of verbs and constructions. Our Bayesian framework is founded on a novel view of constructions as a probabilistic association between syntactic and semantic features. The computational experiments reported here demonstrate the feasibility of learning general constructions, and their exceptions, from individual usages of verbs. The behaviour of the model over the timecourse of acquisition mimics in relevant aspects the stages of learning exhibited by children. Our proposal thus sheds light on the possible mechanisms at work in forming linguistic generalizations and maintaining knowledge of exceptions.