Abstract
In recent years, data augmentation has become an important field of machine learning. While images can use simple techniques such as cropping or rotating, textual data augmentation needs more complex manipulations to ensure that the generated examples are useful. Variational auto-encoders (VAE) and its conditional variant the Conditional-VAE (CVAE) are often used to generate new textual data, both relying on a good enough training of the generator so that it doesn’t create examples of the wrong class. In this paper, we explore a simpler way to use VAE for data augmentation: the training of one VAE per class. We show on several dataset sizes, as well as on four different binary classification tasks, that it systematically outperforms other generative data augmentation techniques.- Anthology ID:
- 2022.coling-1.305
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3454–3464
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.305
- DOI:
- Cite (ACL):
- Frédéric Piedboeuf and Philippe Langlais. 2022. Effective Data Augmentation for Sentence Classification Using One VAE per Class. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3454–3464, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Effective Data Augmentation for Sentence Classification Using One VAE per Class (Piedboeuf & Langlais, COLING 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.coling-1.305.pdf
- Data
- SST, SST-2