  Traditional data augmentation aims to increase the coverage of the input
distribution by generating augmented examples that strongly resemble original
samples in an online fashion where augmented examples dominate training. In
this paper, we propose an alternative perspective -- a multi-task view (MTV) of
data augmentation -- in which the primary task trains on original examples and
the auxiliary task trains on augmented examples. In MTV data augmentation, both
original and augmented samples are weighted substantively during training,
relaxing the constraint that augmented examples must resemble original data and
thereby allowing us to apply stronger levels of augmentation. In empirical
experiments using four common data augmentation techniques on three benchmark
text classification datasets, we find that the MTV leads to higher and more
robust performance improvements than traditional augmentation.
