A probabilistic model of early argument structure acquisition
Speaker: Afra Alishahi
Institution: Saarland University
Abstract:
I will present a probabilistic usage-based model of verb argument structure acquisition that can successfully learn abstract knowledge of language from instances of verb usage, and use this knowledge in various language tasks. The model demonstrates the feasibility of a usage-based account of language learning, and provides concrete explanation for the observed patterns in child language acquisition. I propose a novel representation for the general constructions of language as probabilistic associations between syntactic and semantic features of a verb usage; these associations generalize over the syntactic patterns and the fine-grained semantics of both the verb and its arguments. The acquisition of constructions is modeled as detecting similar usages and grouping them together. We use a probabilistic measure of similarity between verb usages, and a Bayesian framework for clustering them. Language use, on the other hand, is modeled as a prediction problem: each language task is viewed as finding the best value for a missing feature in a usage, based on the available features in that same usage and the acquired knowledge of language so far. In formulating prediction, we use the same Bayesian framework as used for learning, a formulation which takes into account both the general knowledge of language (i.e., constructions) and the specific behaviour of each verb. We show through computational simulation that the behaviour of the model mirrors that of young children in some relevant aspects. The model goes through the same learning stages as children do: the conservative use of the more frequent usages for each individual verb at the beginning, followed by a phase when general patterns are grasped and applied overtly, which leads to occasional overgeneralization errors. Such errors cease to be made over time as the model processes more input.
