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
this https URL

通过 SSMix，在输入文本上进行数据增强而不如之前的隐式向量操作，维持两个原始文本的局部性通过跨度混合以及保留更多与预测相关的标记，我们实验证明，相较于以前的隐式向量操作，我们的方法在包括文本蕴涵、情感分类和问句类型分类的广泛文本分类基准测试上具有更好的性能。