EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification

Minyi Zhao, Lu Zhang, Yi Xu, Jiandong Ding, Jihong Guan, Shuigeng Zhou


Abstract
Recent works have empirically shown the effectiveness of data augmentation (DA) in NLP tasks, especially for those suffering from data scarcity. Intuitively, given the size of generated data, their diversity and quality are crucial to the performance of targeted tasks. However, to the best of our knowledge, most existing methods consider only either the diversity or the quality of augmented data, thus cannot fully mine the potential of DA for NLP. In this paper, we present an easy and plug-in data augmentation framework EPiDA to support effective text classification. EPiDA employs two mechanisms: relative entropy maximization (REM) and conditional entropy minimization (CEM) to control data generation, where REM is designed to enhance the diversity of augmented data while CEM is exploited to ensure their semantic consistency. EPiDA can support efficient and continuous data generation for effective classifier training. Extensive experiments show that EPiDA outperforms existing SOTA methods in most cases, though not using any agent networks or pre-trained generation networks, and it works well with various DA algorithms and classification models.
Anthology ID:
2022.naacl-main.349
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4742–4752
Language:
URL:
https://aclanthology.org/2022.naacl-main.349
DOI:
10.18653/v1/2022.naacl-main.349
Bibkey:
Cite (ACL):
Minyi Zhao, Lu Zhang, Yi Xu, Jiandong Ding, Jihong Guan, and Shuigeng Zhou. 2022. EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4742–4752, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification (Zhao et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.349.pdf
Software:
 2022.naacl-main.349.software.zip
Code
 zhaominyiz/epida
Data
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