Nonparametric Learning of Phonological Constraints in Optimality Theory
Gabriel Doyle, Klinton Bicknell, Roger Lévy · 2014
We present a method to jointly learn fea-tures and weights directly from distri-butional data in a log-linear framework. Specifically, we propose a non-parametric Bayesian model for learning phonologi-cal 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 log-linear method, and is the first algorithm for learning phonological constraints with-out 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 struc-ture corresponding to the standard con-straints. These results suggest an alterna-tive data-driven source for constraints in-stead of a fully innate constraint set. 1