  Data augmentation with mixup has shown to be effective on various computer
vision tasks. Despite its great success, there has been a hurdle to apply mixup
to NLP tasks since text consists of discrete tokens with variable length. In
this work, we propose SSMix, a novel mixup method where the operation is
performed on input text rather than on hidden vectors like previous approaches.
SSMix synthesizes a sentence while preserving the locality of two original
texts by span-based mixing and keeping more tokens related to the prediction
relying on saliency information. With extensive experiments, we empirically
validate that our method outperforms hidden-level mixup methods on a wide range
of text classification benchmarks, including textual entailment, sentiment
classification, and question-type classification. Our code is available at
https://github.com/clovaai/ssmix.
