2021/01/14 by Jason Wei, Wei, Jason, Chengyu Huang +5 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #cs.CL
paper · pdf · doi:10.48550/arxiv.2101.05469
Accepted to EACL 2021
arxiv created 2021/01/14 · arxiv updated 2021/01/15
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.