We study a family of data augmentation methods, substructure substitution
(SUB2), for natural language processing (NLP) tasks. SUB2 generates new
examples by substituting substructures (e.g., subtrees or subsequences) with
ones with the same label, which can be applied to many structured NLP tasks
such as part-of-speech tagging and parsing. For more general tasks (e.g., text
classification) which do not have explicitly annotated substructures, we
present variations of SUB2 based on constituency parse trees, introducing
structure-aware data augmentation methods to general NLP tasks. For most cases,
training with the augmented dataset by SUB2 achieves better performance than
training with the original training set. Further experiments show that SUB2 has
more consistent performance than other investigated augmentation methods,
across different tasks and sizes of the seed dataset.

该研究探讨了一种基于数据增强的方法 SUB2，可以用于自然语言处理 (NLP) 任务，通过替换子结构生成新的样例，可以应用于许多结构化 NLP 任务，同时针对一般性的任务，该方法也可以通过基于句法解析树的变化引入结构感知型数据增强方法，大部分情况下，SUB2 通过增强数据集的训练可以获得比使用原始数据集更好的性能，并且在不同类型的任务和种子数据集大小方面，SUB2 相比其他方法具有更加一致的性能。