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We propose a novel data augmentation for labeled sentences called contextualaugmentation. We assume an invariance that sentences are natural even if thewords in the sentences are replaced with other words with paradigmaticrelations. We stochastically replace words with other words that are predictedby a bi-directional language model at the word positions. Words predictedaccording to a context are numerous but appropriate for the augmentation of theoriginal words. Furthermore, we retrofit a language model with alabel-conditional architecture, which allows the model to augment sentenceswithout breaking the label-compatibility. Through the experiments for sixvarious different text classification tasks, we demonstrate that the proposedmethod improves classifiers based on the convolutional or recurrent neuralnetworks.

Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations