Text Augmentation in a Multi-Task View

By Jason Wei, Chengyu Huang, Shiqi Xu, Soroush Vosoughi. 2021

14 citations recorded by the source corpus.

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 augmentation functions. In empirical experiments using four common data augmentation techniques on three benchmark text classification datasets, we find that using the MTV leads to higher and more robust performance than traditional augmentation.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, background): “but usually offer incremental performance improvements (Li et al., 2017; Wei and Zou, 2019; Wei et al., 2021b).”
Text Data Augmentation for Deep Learning (2021, background): “Wei et al. [130] propose an interesting extension, named as Multi-Task View (MTV), to the common practice of transfer learning to better utilize augmented subsets and share information across distributions.”