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        <title>EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
%A Wei, Jason
%A Zou, Kai
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F wei-zou-2019-eda
%X 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.
%R 10.18653/v1/D19-1670
%U https://aclanthology.org/D19-1670
%U https://doi.org/10.18653/v1/D19-1670
%P 6382-6388

Markdown (Informal)

[EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://aclanthology.org/D19-1670) (Wei & Zou, EMNLP-IJCNLP 2019)

• EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (Wei & Zou, EMNLP-IJCNLP 2019)

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.