  Mixup, a recent proposed data augmentation method through linearly
interpolating inputs and modeling targets of random samples, has demonstrated
its capability of significantly improving the predictive accuracy of the
state-of-the-art networks for image classification. However, how this technique
can be applied to and what is its effectiveness on natural language processing
(NLP) tasks have not been investigated. In this paper, we propose two
strategies for the adaption of Mixup on sentence classification: one performs
interpolation on word embeddings and another on sentence embeddings. We conduct
experiments to evaluate our methods using several benchmark datasets. Our
studies show that such interpolation strategies serve as an effective, domain
independent data augmentation approach for sentence classification, and can
result in significant accuracy improvement for both CNN and LSTM models.
