Nonparametric learning of phonological constraints in optimality theory

We present a method to jointly learn features and weights directly from distributional data in a log-linear framework. Specifically, we propose a non-parametric Bayesian model for learning phonological markedness constraints directly from the distribution of input-output mappings in an Optimality Theory (OT) setting. The model uses an Indian Buffet Process prior to learn the feature values used in the loglinear method, and is the first algorithm for learning phonological constraints without presupposing constraint structure. The model learns a system of constraints that explains observed data as well as the phonologically-grounded constraints of a standard analysis, with a violation structure corresponding to the standard constraints. These results suggest an alternative data-driven source for constraints instead of a fully innate constraint set.