@InProceedings{jin-EtAl:2017:BEA,
  author    = {Jin, Lifeng  and  White, Michael  and  Jaffe, Evan  and  Zimmerman, Laura  and  Danforth, Douglas},
  title     = {Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System},
  booktitle = {Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {11--21},
  abstract  = {For medical students, virtual patient dialogue systems can provide useful
	training opportunities without the cost of employing actors to portray
	standardized patients.              This work utilizes word- and
	character-based convolutional neural networks (CNNs) for question
	identification in a virtual
	patient dialogue system, outperforming a strong word- and character-based
	logistic regression baseline.  While the CNNs perform well given sufficient
	training data, the best system performance is ultimately achieved by combining
	CNNs with a hand-crafted pattern matching system that is robust to label
	sparsity, providing a 10% boost in system accuracy and an error reduction of
	47% as compared to the pattern-matching system alone.},
  url       = {http://www.aclweb.org/anthology/W17-5002}
}