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To solve the problem of poor performance of deep neural network models due toinsufficient data, a simple yet effective interpolation-based data augmentationmethod is proposed: MSMix (Manifold Swap Mixup). This method feeds twodifferent samples to the same deep neural network model, and then randomlyselect a specific layer and partially replace hidden features at that layer ofone of the samples by the counterpart of the other. The mixed hidden featuresare fed to the model and go through the rest of the network. Two differentselection strategies are also proposed to obtain richer hidden representation.Experiments are conducted on three Chinese intention recognition datasets, andthe results show that the MSMix method achieves better results than othermethods in both full-sample and small-sample configurations.

MSMix:An Interpolation-Based Text Data Augmentation Method Manifold Swap Mixup