BEA-13 paper on using paraphrasing and neural memory-based classification in a virtual patient dialogue system

Lifeng Jin, David King, Amad Hussein, Doug Danforth and I have found that to tackle the long tail of relatively infrequently asked questions in a virtual patient dialogue system, it pays to combine paraphrasing for data augmentation with neural memory-based classification, as together the two methods yield a nearly 10% absolute improvement in accuracy on the least frequently asked questions. The paper will appear next week at the 13th Workshop on Innovative Use of NLP for Building Educational Applications at NAACL HLT 2018 in New Orleans.