@inproceedings{jin-etal-2017-combining-cnns,
    title = "Combining {CNN}s and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System",
    author = "Jin, Lifeng  and
      White, Michael  and
      Jaffe, Evan  and
      Zimmerman, Laura  and
      Danforth, Douglas",
    editor = "Tetreault, Joel  and
      Burstein, Jill  and
      Leacock, Claudia  and
      Yannakoudakis, Helen",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-5002",
    doi = "10.18653/v1/W17-5002",
    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.",
}