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The vast majority of text transformation techniques in NLP are inherentlylimited in their ability to expand input space coverage due to an implicitconstraint to preserve the original class label. In this work, we propose thenotion of sibylvariance (SIB) to describe the broader set of transforms thatrelax the label-preserving constraint, knowably vary the expected class, andlead to significantly more diverse input distributions. We offer a unifiedframework to organize all data transformations, including two types of SIB: (1)Transmutations convert one discrete kind into another, (2) Mixture Mutationsblend two or more classes together. To explore the role of sibylvariance withinNLP, we implemented 41 text transformations, including several novel techniqueslike Concept2Sentence and SentMix. Sibylvariance also enables a unique form ofadaptive training that generates new input mixtures for the most confused classpairs, challenging the learner to differentiate with greater nuance. Ourexperiments on six benchmark datasets strongly support the efficacy ofsibylvariance for generalization performance, defect detection, and adversarialrobustness.

Sibylvariant Transformations for Robust Text Classification