@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.",
}
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%0 Conference Proceedings
%T Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System
%A Jin, Lifeng
%A White, Michael
%A Jaffe, Evan
%A Zimmerman, Laura
%A Danforth, Douglas
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F jin-etal-2017-combining-cnns
%X 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.
%R 10.18653/v1/W17-5002
%U https://aclanthology.org/W17-5002
%U https://doi.org/10.18653/v1/W17-5002
%P 11-21
Markdown (Informal)
[Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System](https://aclanthology.org/W17-5002) (Jin et al., BEA 2017)
ACL