@InProceedings{perkins-feldman-lidz:2017:CMCL,
  author    = {Perkins, Laurel  and  Feldman, Naomi  and  Lidz, Jeffrey},
  title     = {Learning an Input Filter for Argument Structure Acquisition},
  booktitle = {Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2017)},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {11--19},
  abstract  = {How do children learn a verb’s argument structure when their input contains
	nonbasic clauses that obscure verb transitivity? Here we present a new model
	that infers verb transitivity by learning to filter out non-basic clauses that
	were likely parsed in error. In simulations with child-directed speech, we show
	that this model accurately categorizes the majority of 50 frequent transitive,
	intransitive and alternating verbs, and jointly learns appropriate parameters
	for filtering parsing errors. Our model is thus able to filter out problematic
	data for verb learning without knowing in advance which data need to be
	filtered.},
  url       = {http://www.aclweb.org/anthology/W17-0702}
}