EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Jason Wei, Kai Zou


Abstract
We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks. EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50% of the available training set achieved the same accuracy as normal training with all available data. We also performed extensive ablation studies and suggest parameters for practical use.
Anthology ID:
D19-1670
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6382–6388
Language:
URL:
https://aclanthology.org/D19-1670
DOI:
10.18653/v1/D19-1670
Bibkey:
Cite (ACL):
Jason Wei and Kai Zou. 2019. EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6382–6388, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (Wei & Zou, EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1670.pdf
Attachment:
 D19-1670.Attachment.zip
Code
 jasonwei20/eda_nlp +  additional community code