  The vast majority of text transformation techniques in NLP are inherently
limited in their ability to expand input space coverage due to an implicit
constraint to preserve the original class label. In this work, we propose the
notion of sibylvariance (SIB) to describe the broader set of transforms that
relax the label-preserving constraint, knowably vary the expected class, and
lead to significantly more diverse input distributions. We offer a unified
framework to organize all data transformations, including two types of SIB: (1)
Transmutations convert one discrete kind into another, (2) Mixture Mutations
blend two or more classes together. To explore the role of sibylvariance within
NLP, we implemented 41 text transformations, including several novel techniques
like Concept2Sentence and SentMix. Sibylvariance also enables a unique form of
adaptive training that generates new input mixtures for the most confused class
pairs, challenging the learner to differentiate with greater nuance. Our
experiments on six benchmark datasets strongly support the efficacy of
sibylvariance for generalization performance, defect detection, and adversarial
robustness.
