Automatic data augmentation is commonly used in computer vision (Simard et al., 1998;Szegedy et al., 2014;Krizhevsky et al., 2017) and speech (Cui et al., 2015;Ko et al., 2015) and can help train more robust models, particularly when using smaller datasets.
because it is challenging to come up with generalized rules for language transformation, universal data augmentation techniques in NLP have not been thoroughly explored.