@InProceedings{jin-EtAl:2018:W18-05,
  author    = {Jin, Lifeng  and  King, David  and  Hussein, Amad  and  White, Michael  and  Danforth, Douglas},
  title     = {Using Paraphrasing and Memory-Augmented Models to Combat Data Sparsity in Question Interpretation with a Virtual Patient Dialogue System},
  booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {13--23},
  abstract  = {When interpreting questions in a virtual patient dialogue system one must inevitably tackle the challenge of a long tail of relatively infrequently asked questions. To make progress on this challenge, we investigate the use of paraphrasing for data augmentation and neural memory-based classification, finding that the two methods work best in combination. In particular, we find that the neural memory-based approach not only outperforms a straight CNN classifier on low frequency questions, but also takes better advantage of the augmented data created by paraphrasing, together yielding a nearly 10\% absolute improvement in accuracy on the least frequently asked questions.},
  url       = {http://www.aclweb.org/anthology/W18-0502}
}