This paper proposes a simple yet effective interpolation-based data
augmentation approach termed DoubleMix, to improve the robustness of models in
text classification. DoubleMix first leverages a couple of simple augmentation
operations to generate several perturbed samples for each training data, and
then uses the perturbed data and original data to carry out a two-step
interpolation in the hidden space of neural models. Concretely, it first mixes
up the perturbed data to a synthetic sample and then mixes up the original data
and the synthetic perturbed data. DoubleMix enhances models' robustness by
learning the "shifted" features in hidden space. On six text classification
benchmark datasets, our approach outperforms several popular text augmentation
methods including token-level, sentence-level, and hidden-level data
augmentation techniques. Also, experiments in low-resource settings show our
approach consistently improves models' performance when the training data is
scarce. Extensive ablation studies and case studies confirm that each component
of our approach contributes to the final performance and show that our approach
exhibits superior performance on challenging counterexamples. Additionally,
visual analysis shows that text features generated by our approach are highly
interpretable. Our code for this paper can be found at
this https URL.

提出了一种名为 DoubleMix 的插值数据增强方法，通过合成的数据集和原始数据集在神经模型的隐藏空间中进行插值，以及学习隐藏空间中的 “偏移” 特征来提高模型的鲁棒性，在六个文本分类基准数据集上，该方法优于几种流行的文本增强技术，同时在低资源情况下也能保持性能的提升。