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.
@article{arxiv.2403.20015,
title = {Adverb Is the Key: Simple Text Data Augmentation with Adverb Deletion},
author = {Juhwan Choi and YoungBin Kim},
journal= {arXiv preprint arXiv:2403.20015},
year = {2024}
}