Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning

Jason Wei, Chengyu Huang, Soroush Vosoughi, Yu Cheng, Shiqi Xu


Abstract
Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. This paper explores data augmentation—a technique particularly suitable for training with limited data—for this few-shot, highly-multiclass text classification setting. On four diverse text classification tasks, we find that common data augmentation techniques can improve the performance of triplet networks by up to 3.0% on average. To further boost performance, we present a simple training strategy called curriculum data augmentation, which leverages curriculum learning by first training on only original examples and then introducing augmented data as training progresses. We explore a two-stage and a gradual schedule, and find that, compared with standard single-stage training, curriculum data augmentation trains faster, improves performance, and remains robust to high amounts of noising from augmentation.
Anthology ID:
2021.naacl-main.434
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5493–5500
Language:
URL:
https://aclanthology.org/2021.naacl-main.434
DOI:
10.18653/v1/2021.naacl-main.434
Bibkey:
Cite (ACL):
Jason Wei, Chengyu Huang, Soroush Vosoughi, Yu Cheng, and Shiqi Xu. 2021. Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5493–5500, Online. Association for Computational Linguistics.
Cite (Informal):
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning (Wei et al., NAACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/2021.naacl-main.434.pdf
Video:
 https://preview.aclanthology.org/author-url/2021.naacl-main.434.mp4
Code
 jasonwei20/triplet-loss
Data
FewRel