AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models

Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, Chao Zhang


Abstract
Although fine-tuning pre-trained language models (PLMs) renders strong performance in many NLP tasks, it relies on excessive labeled data. Recently, researchers have resorted to active fine-tuning for enhancing the label efficiency of PLM fine-tuning, but existing methods of this type usually ignore the potential of unlabeled data. We develop AcTune, a new framework that improves the label efficiency of active PLM fine-tuning by unleashing the power of unlabeled data via self-training. AcTune switches between data annotation and model self-training based on uncertainty: the unlabeled samples of high-uncertainty are selected for annotation, while the ones from low-uncertainty regions are used for model self-training. Additionally, we design (1) a region-aware sampling strategy to avoid redundant samples when querying annotations and (2) a momentum-based memory bank to dynamically aggregate the model’s pseudo labels to suppress label noise in self-training. Experiments on 6 text classification datasets show that AcTune outperforms the strongest active learning and self-training baselines and improves the label efficiency of PLM fine-tuning by 56.2% on average. Our implementation is available at https://github.com/yueyu1030/actune.
Anthology ID:
2022.naacl-main.102
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1422–1436
Language:
URL:
https://aclanthology.org/2022.naacl-main.102
DOI:
10.18653/v1/2022.naacl-main.102
Bibkey:
Cite (ACL):
Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, and Chao Zhang. 2022. AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1422–1436, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models (Yu et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.102.pdf
Code
 yueyu1030/actune
Data
AG NewsPubMed RCTSSTWrench