方法对比

	弱监督BERT分类	基于领域自适应BERT的分类
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2017–2020	2019–2020
提出者	Multiple (Ratner et al. for weak supervision framework; Meng et al. for BERT integration)	Gururangan et al. (2020); earlier domain-specific instances include Lee et al. (2020) — BioBERT
类型	Weakly supervised fine-tuning of pre-trained language model	Domain-adaptive pre-training followed by supervised fine-tuning
开创性文献	Meng, Y., Zhang, Y., Huang, J., Xiong, C., Ji, H., Zhang, C., & Han, J. (2020). Text Classification Using Label Names Only: A Language Model Self-Training Approach. Proceedings of EMNLP 2020, 9006–9017.	Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 8342–8360.
别名	WS-BERT, BERT with weak supervision, label-efficient BERT classification, noisy-label BERT fine-tuning	DAPT BERT classification, domain-adaptive pre-training, domain-specific BERT fine-tuning, BERT DAPT
相关	6	6
摘要	Weakly supervised BERT-based classification adapts BERT to text classification tasks when only noisy, heuristic, or programmatically generated labels are available instead of clean human annotations. It combines weak supervision frameworks — such as labeling functions and data programming — with BERT's pre-trained language representations to achieve robust classification without expensive hand-labeling.	Domain-adaptive BERT-based classification extends the standard fine-tuning pipeline by first continuing BERT's masked-language-model pre-training on a large corpus of in-domain unlabeled text, then fine-tuning the adapted model on labeled examples for the target classification task. This two-stage approach closes the vocabulary and distributional gap between BERT's general pre-training corpus and specialized domains such as biomedicine, law, finance, or social-media text.