Text Augmentation Using Dataset Reconstruction for Low-Resource Classification
Adir Rahamim, Guy Uziel, Esther Goldbraich, Ateret Anaby Tavor
Abstract
In the deployment of real-world text classification models, label scarcity is a common problem and as the number of classes increases, this problem becomes even more complex. An approach to addressing this problem is by applying text augmentation methods. One of the more prominent methods involves using the text-generation capabilities of language models. In this paper, we propose Text AUgmentation by Dataset Reconstruction (TAU-DR), a novel method of data augmentation for text classification. We conduct experiments on several multi-class datasets, showing that our approach improves the current state-of-the-art techniques for data augmentation.- Anthology ID:
- 2023.findings-acl.466
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7389–7402
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.466
- DOI:
- 10.18653/v1/2023.findings-acl.466
- Cite (ACL):
- Adir Rahamim, Guy Uziel, Esther Goldbraich, and Ateret Anaby Tavor. 2023. Text Augmentation Using Dataset Reconstruction for Low-Resource Classification. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7389–7402, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Text Augmentation Using Dataset Reconstruction for Low-Resource Classification (Rahamim et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.findings-acl.466.pdf