@InProceedings{doucette:2017:CMCL,
  author    = {Doucette, Amanda},
  title     = {Inherent Biases of Recurrent Neural Networks for Phonological Assimilation and Dissimilation},
  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     = {35--40},
  abstract  = {A recurrent neural network model of phonological pattern learning is proposed.
	The model is a relatively simple neural network with one recurrent layer, and
	displays biases in learning that mimic observed biases in human learning.
	Single-feature patterns are learned faster than two-feature patterns, and vowel
	or consonant-only patterns are learned faster than patterns involving vowels
	and consonants, mimicking the results of laboratory learning experiments. In
	non-recurrent models, capturing these biases requires the use of alpha features
	or some other representation of repeated features, but with a recurrent neural
	network, these elaborations are not necessary.},
  url       = {http://www.aclweb.org/anthology/W17-0705}
}