Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning

Seonghyeon Ye, Jiseon Kim, Alice Oh


Abstract
We introduce EfficientCL, a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning. For data augmentation, we stack two types of operation sequentially: cutoff and PCA jittering. While pretraining steps proceed, we apply curriculum learning by incrementing the augmentation degree for each difficulty step. After data augmentation is finished, contrastive learning is applied on projected embeddings of original and augmented examples. When finetuned on GLUE benchmark, our model outperforms baseline models, especially for sentence-level tasks. Additionally, this improvement is capable with only 70% of computational memory compared to the baseline model.
Anthology ID:
2021.emnlp-main.138
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1832–1838
Language:
URL:
https://aclanthology.org/2021.emnlp-main.138
DOI:
10.18653/v1/2021.emnlp-main.138
Bibkey:
Cite (ACL):
Seonghyeon Ye, Jiseon Kim, and Alice Oh. 2021. Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1832–1838, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning (Ye et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.138.pdf
Software:
 2021.emnlp-main.138.Software.zip
Video:
 https://aclanthology.org/2021.emnlp-main.138.mp4
Code
 vano1205/efficientcl