Interpolation-based Data Augmentation (DA) methods (Mixup) linearly
interpolate the inputs and labels of two or more training examples. Mixup has
more recently been adapted to the field of Natural Language Processing (NLP),
mainly for sequence labeling tasks. However, such a simple adoption yields
mixed or unstable improvements over the baseline models. We argue that the
direct-adoption methods do not account for structures in NLP tasks. To this
end, we propose SegMix, a collection of interpolation-based DA algorithms that
can adapt to task-specific structures. SegMix poses fewer constraints on data
structures, is robust to various hyperparameter settings, applies to more task
settings, and adds little computational overhead. In the algorithm's core, we
apply interpolation methods on task-specific meaningful segments, in contrast
to applying them on sequences as in prior work. We find SegMix to be a flexible
framework that combines rule-based DA methods with interpolation-based methods,
creating interesting mixtures of DA techniques. We show that SegMix
consistently improves performance over strong baseline models in Named Entity
Recognition (NER) and Relation Extraction (RE) tasks, especially under
data-scarce settings. Furthermore, this method is easy to implement and adds
negligible training overhead.

提出了一种基于插值的数据增强算法 SegMix，该算法在具有任务特定结构的有意义的片段上进行插值，相比之前的方法在命名实体识别（NER）和关系抽取（RE）任务中表现更好，尤其在数据稀缺的情况下性能提升明显。

SegMix：一种简单的结构感知数据增强方法

SegMix: A Simple Structure-Aware Data Augmentation Method

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。