Paper

Modeling the Learning of the Person Case Constraint

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Abstract

Many domains of linguistic research posit feature bundles as an explanation for various phenomena. Such hypotheses are often evaluated on their simplicity (or parsimony). We take a complementary approach. Specifically, we evaluate different hypotheses about the representation of person features in syntax on the basis of their implications for learning the Person Case Constraint (PCC). The PCC refers to a phenomenon where certain combinations of clitics (pronominal bound morphemes) are disallowed with ditransitive verbs. We compare a simple theory of the PCC, where person features are represented as atomic units, to a feature-based theory of the PCC, where person features are represented as feature bundles. We use Bayesian modeling to compare these theories, using data based on realistic proportions of clitic combinations from child-directed speech. We find that both theories can learn the target grammar given enough data, but that the feature-based theory requires significantly less data, suggesting that developmental trajectories could provide insight into syntactic representations in this domain.

Keywords: learning, clitics, Bayesian modeling, Person Case Constraint, morphosyntax

How to Cite:

Liter, A. & Feldman, N. H., (2020) “Modeling the Learning of the Person Case Constraint”, Society for Computation in Linguistics 3(1), 372-381. doi: https://doi.org/10.7275/38dj-hj49