Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System

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 characterbased 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.