@InProceedings{feldman:2017:CoNLL,
  author    = {Feldman, Naomi},
  title     = {Rational Distortions of Learners' Linguistic Input},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {2},
  abstract  = {Language acquisition can be modeled as a statistical inference problem:
	children use sentences and sounds in their input to infer linguistic structure.
	 However, in many cases, children learn from data whose statistical structure
	is distorted relative to the language they are learning.  Such distortions can
	arise either in the input itself, or as a result of children's immature
	strategies for encoding their input.  This work examines several cases in which
	the statistical structure of children's input differs from the language being
	learned.  Analyses show that these distortions of the input can be accounted
	for with a statistical learning framework by carefully considering the
	inference problems that learners solve during language acquisition},
  url       = {http://aclweb.org/anthology/K17-1002}
}