@InProceedings{doyle-levy:2016:COLING,
  author    = {Doyle, Gabriel  and  Levy, Roger},
  title     = {Data-driven learning of symbolic constraints for a log-linear model in a phonological setting},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2217--2226},
  abstract  = {We propose a non-parametric Bayesian model for learning and weighting
	symbolically-defined constraints to populate a log-linear model.  The model
	jointly infers a vector of binary constraint values for each candidate output
	and likely definitions for these constraints, combining observations of the
	output classes with a (potentially infinite) grammar over potential constraint
	definitions.  We present results on a small morphophonological system, English
	regular plurals, as a test case.  The inferred constraints, based on a grammar
	of articulatory features, perform as well as theoretically-defined constraints
	on both observed and novel forms of English regular plurals. The learned
	constraint values and definitions also closely resemble standard constraints
	defined within phonological theory.},
  url       = {http://aclweb.org/anthology/C16-1209}
}