This paper investigates the effectiveness of token-level text augmentation
and the role of probabilistic linguistic knowledge within a
linguistically-motivated evaluation context. Two text augmentation programs,
REDA and REDA$_{NG}$, were developed, both implementing five token-level text
editing operations: Synonym Replacement (SR), Random Swap (RS), Random
Insertion (RI), Random Deletion (RD), and Random Mix (RM). REDA$_{NG}$
leverages pretrained $n$-gram language models to select the most likely
augmented texts from REDA's output. Comprehensive and fine-grained experiments
were conducted on a binary question matching classification task in both
Chinese and English. The results strongly refute the general effectiveness of
the five token-level text augmentation techniques under investigation, whether
applied together or separately, and irrespective of various common
classification model types used, including transformers. Furthermore, the role
of probabilistic linguistic knowledge is found to be minimal.

本文研究了基于标记级别的文本增强及概率语言知识在基于语言学评估的情境下的作用。在中英文的问题匹配分类任务中，对五种标记级别的文本增强技术进行了全面细致的实验，发现它们的效果普遍较差且概率语言知识的作用极小。