  Text classification is a representative downstream task of natural language
processing, and has exhibited excellent performance since the advent of
pre-trained language models based on Transformer architecture. However, in
pre-trained language models, under-fitting often occurs due to the size of the
model being very large compared to the amount of available training data. Along
with significant importance of data collection in modern machine learning
paradigm, studies have been actively conducted for natural language data
augmentation. In light of this, we introduce three data augmentation schemes
that help reduce underfitting problems of large-scale language models.
Primarily we use a generation model for data augmentation, which is defined as
Data Augmentation with Generation (DAG). Next, we augment data using text
modification techniques such as corruption and word order change (Data
Augmentation with Modification, DAM). Finally, we propose Data Augmentation
with Generation And Modification (DAGAM), which combines DAG and DAM techniques
for a boosted performance. We conduct data augmentation for six benchmark
datasets of text classification task, and verify the usefulness of DAG, DAM,
and DAGAM through BERT-based fine-tuning and evaluation, deriving better
results compared to the performance with original datasets.
