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。