Paper: Nonparametric Learning of Phonological Constraints in Optimality Theory

ACL ID P14-1103
Title Nonparametric Learning of Phonological Constraints in Optimality Theory
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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 da...