Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks

Xing Wu, Chaochen Gao, Meng Lin, Liangjun Zang, Songlin Hu


Abstract
Before entering the neural network, a token needs to be converted to its one-hot representation, which is a discrete distribution of the vocabulary. Smoothed representation is the probability of candidate tokens obtained from the pre-trained masked language model, which can be seen as a more informative augmented substitution to the one-hot representation. We propose an efficient data augmentation method, dub as text smoothing, by converting a sentence from its one-hot representation to controllable smoothed representation.We evaluate text smoothing on different datasets in a low-resource regime. Experimental results show that text smoothing outperforms various mainstream data augmentation methods by a substantial margin. Moreover, text smoothing can be combined with these data augmentation methods to achieve better performance.
Anthology ID:
2022.acl-short.97
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
871–875
Language:
URL:
https://aclanthology.org/2022.acl-short.97
DOI:
10.18653/v1/2022.acl-short.97
Bibkey:
Cite (ACL):
Xing Wu, Chaochen Gao, Meng Lin, Liangjun Zang, and Songlin Hu. 2022. Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 871–875, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks (Wu et al., ACL 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.acl-short.97.pdf
Code
 caskcsg/TextSmoothing
Data
SNIPSSST