Learning verb alternations in a usage-based Bayesian model

One of the key debates in language acquisition involves the degree to which children’s early linguistic knowledge employs abstract representations. While usage-based accounts that focus on input-driven learning have gained prominence, it remains an open question how such an approach can explain the evidence for children’s apparent use of abstract syntactic generalizations. We develop a novel hierarchical Bayesian model that demonstrates how abstract knowledge can be generalized from usage-based input. We demonstrate the model on the learning of verb alternations, showing that such a usage-based model must allow for the inference of verb class structure, not simply the inference of individual constructions, in order to account for the acquisition of alternations.