# SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels

arXiv: https://arxiv.org/abs/2402.05591 · HTML: https://mlsanity.com/paper/2402.05591/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [Synthetic and Natural Noise Both Break Neural Machine Translation](https://mlsanity.com/paper/1711.02173/) (2017)
- [AEDA: An Easier Data Augmentation Technique for Text Classification](https://mlsanity.com/paper/2108.13230/) (2021)

## Built on by (most cited first, 3 of 3)

- [Colorful Cutout: Enhancing Image Data Augmentation with Curriculum Learning](https://mlsanity.com/paper/2403.20012/) (2024)
- [AugmenTory: A Fast and Flexible Polygon Augmentation Library](https://mlsanity.com/paper/2405.04442/) (2024)
- [Adverb Is the Key: Simple Text Data Augmentation with Adverb Deletion](https://mlsanity.com/paper/2403.20015/) (2024)

## Abstract (arXiv)

Rule-based text data augmentation is widely used for NLP tasks due to its simplicity. However, this method can potentially damage the original meaning of the text, ultimately hurting the performance of the model. To overcome this limitation, we propose a straightforward technique for applying soft labels to augmented data. We conducted experiments across seven different classification tasks and empirically demonstrated the effectiveness of our proposed approach. We have publicly opened our source code for reproducibility.