Adverb Is the Key: Simple Text Data Augmentation with Adverb Deletion
This work addresses a specific issue in text data augmentation for NLP applications, but it is incremental as it builds on existing rule-based methods with a minor modification.
The paper tackles the problem of semantic loss in rule-based text data augmentation by proposing a simple adverb deletion strategy, achieving efficient and effective results in text classification and natural language inference tasks.
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.