Douglas Danforth


2018

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Using Paraphrasing and Memory-Augmented Models to Combat Data Sparsity in Question Interpretation with a Virtual Patient Dialogue System
Lifeng Jin | David King | Amad Hussein | Michael White | Douglas Danforth
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

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.

2017

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Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System
Lifeng Jin | Michael White | Evan Jaffe | Laura Zimmerman | Douglas Danforth
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

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.

2016

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A Corpus of Word-Aligned Asked and Anticipated Questions in a Virtual Patient Dialogue System
Ajda Gokcen | Evan Jaffe | Johnsey Erdmann | Michael White | Douglas Danforth
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present a corpus of virtual patient dialogues to which we have added manually annotated gold standard word alignments. Since each question asked by a medical student in the dialogues is mapped to a canonical, anticipated version of the question, the corpus implicitly defines a large set of paraphrase (and non-paraphrase) pairs. We also present a novel process for selecting the most useful data to annotate with word alignments and for ensuring consistent paraphrase status decisions. In support of this process, we have enhanced the earlier Edinburgh alignment tool (Cohn et al., 2008) and revised and extended the Edinburgh guidelines, in particular adding guidance intended to ensure that the word alignments are consistent with the overall paraphrase status decision. The finished corpus and the enhanced alignment tool are made freely available.

2015

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Interpreting Questions with a Log-Linear Ranking Model in a Virtual Patient Dialogue System
Evan Jaffe | Michael White | William Schuler | Eric Fosler-Lussier | Alex Rosenfeld | Douglas Danforth
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications