Sungjae Hwang
2024
All You Need is Attention: Lightweight Attention-based Data Augmentation for Text Classification
Junehyung Kim
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Sungjae Hwang
Findings of the Association for Computational Linguistics: EMNLP 2024
This paper introduces LADAM, a novel method for enhancing the performance of text classification tasks. LADAM employs attention mechanisms to exchange semantically similar words between sentences. This approach generates a greater diversity of synthetic sentences compared to simpler operations like random insertions, while maintaining the context of the original sentences. Additionally, LADAM is an easy-to-use, lightweight technique that does not require external datasets or large language models. Our experimental results across five datasets demonstrate that LADAM consistently outperforms baseline methods across diverse text classification conditions.