  In the field of text data augmentation, rule-based methods are widely adopted
for real-world applications owing to their cost-efficiency. However,
conventional rule-based approaches suffer from the possibility of losing the
original semantics of the given text. We propose a novel text data augmentation
strategy that avoids such phenomena through a straightforward deletion of
adverbs, which play a subsidiary role in the sentence. Our comprehensive
experiments demonstrate the efficiency and effectiveness of our proposed
approach for not just single text classification, but also natural language
inference that requires semantic preservation. We publicly released our source
code for reproducibility.
