SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels
Juhwan Choi Kyohoon Jin Junho Lee Sangmin Song Youngbin Kim
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.