@InProceedings{johansen-socher:2017:RepL4NLP,
  author    = {Johansen, Alexander  and  Socher, Richard},
  title     = {Learning when to skim and when to read},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {257--264},
  abstract  = {Many recent advances in deep learning for natural language processing have come
	at increasing computational cost, but the power of these state-of-the-art
	models is not needed for every example in a dataset. We demonstrate two
	approaches to reducing unnecessary computation in cases where a fast but weak
	baseline classier and a stronger, slower model are both available. Applying an
	AUC-based metric to the task of sentiment classification, we find significant
	efficiency gains with both a probability-threshold method for reducing
	computational cost and one that uses a secondary decision network.},
  url       = {http://www.aclweb.org/anthology/W17-2631}
}