BEA-12 paper on using CNNs in a virtual patient dialogue system; Robots Podcast interview on MSLD poster

My Robots Podcast interview about the poster Evan Jaffe and I presented at Midwest Speech and Language Days 2017 on question interpretation in a virtual patient dialogue system has appeared. Since then, Lifeng Jin has led our subsequent work on using a CNN to improve upon a strong logistic regression baseline, yielding a remarkable 47% error reduction when used in combination with an existing pattern-matching system. The paper will appear at the 12th Workshop on Innovative Use of NLP for Building Educational Applications at EMNLP 2017 in Copenhagen.

Midwest Speech and Language Days 2017 poster on question interpretation in a virtual patient dialogue system

Evan Jaffe and I presented a poster (with Laura Zimmerman and Douglas Danforth) on question interpretation in a virtual patient dialogue system at Midwest Speech and Language Days 2017 in Chicago demonstrating a remarkable error reduction when combining a pattern-matching system with one using logistic regression, also motivating the need to use paraphrasing for data augmentation.

NSF project on paraphrasing and discriminative ASR for question-answering dialogue systems

I’m thrilled to announce that I’ll have the opportunity to continue collaborating with Eric Fosler-Lussier, Douglas Danforth, William Schuler, Evan Jaffe and others here on question-answering dialogue systems (e.g. with virtual patients) thanks to new NSF funding. The idea is to use paraphrasing to semi-automatically expand the set of known question variants in order to enhance the robustness of interpretation and enable more accurate speech recognition via discriminative training techniques.