  This paper asks whether extrapolating the hidden space distribution of text
examples from one class onto another is a valid inductive bias for data
augmentation. To operationalize this question, I propose a simple data
augmentation protocol called "good-enough example extrapolation" (GE3). GE3 is
lightweight and has no hyperparameters. Applied to three text classification
datasets for various data imbalance scenarios, GE3 improves performance more
than upsampling and other hidden-space data augmentation methods.
