%0 Conference Proceedings %T Using Paraphrasing and Memory-Augmented Models to Combat Data Sparsity in Question Interpretation with a Virtual Patient Dialogue System %A Jin, Lifeng %A King, David %A Hussein, Amad %A White, Michael %A Danforth, Douglas %Y Tetreault, Joel %Y Burstein, Jill %Y Kochmar, Ekaterina %Y Leacock, Claudia %Y Yannakoudakis, Helen %S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications %D 2018 %8 June %I Association for Computational Linguistics %C New Orleans, Louisiana %F jin-etal-2018-using %X 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. %R 10.18653/v1/W18-0502 %U https://aclanthology.org/W18-0502 %U https://doi.org/10.18653/v1/W18-0502 %P 13-23