Wednesday, June 12, 2019 12:15 PM EDT
Alireza Heidari, PhD candidate
David R. Cheriton School of Computer Science
We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement.
