@InProceedings{kazi-thompson:2017:Short,
  author    = {Kazi, Michaeel  and  Thompson, Brian},
  title     = {Implicitly-Defined Neural Networks for Sequence Labeling},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {172--177},
  abstract  = {In this work, we propose a novel, implicitly-defined neural network
	architecture
	and describe a method to compute its components.
	The proposed architecture forgoes the causality assumption
	used to formulate recurrent neural networks
	and instead couples the hidden states of the network,
	allowing improvement on problems with complex, long-distance dependencies.
	Initial experiments demonstrate the new architecture outperforms both the
	Stanford Parser
	and baseline bidirectional networks on the Penn Treebank Part-of-Speech tagging
	task
	and a baseline bidirectional network on an additional artificial random biased
	walk task.},
  url       = {http://aclweb.org/anthology/P17-2027}
}