MSMix:An Interpolation-Based Text Data Augmentation Method Manifold Swap
  Mixup
Mao Ye Haitao Wang Zheqian Chen
To solve the problem of poor performance of deep neural network models due to
insufficient data, a simple yet effective interpolation-based data augmentation
method is proposed: MSMix (Manifold Swap Mixup). This method feeds two
different samples to the same deep neural network model, and then randomly
select a specific layer and partially replace hidden features at that layer of
one of the samples by the counterpart of the other. The mixed hidden features
are fed to the model and go through the rest of the network. Two different
selection strategies are also proposed to obtain richer hidden representation.
Experiments are conducted on three Chinese intention recognition datasets, and
the results show that the MSMix method achieves better results than other
methods in both full-sample and small-sample configurations.