방법 비교

	약한 지도 BERT 기반 분류	RoBERTa 기반 분류
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2017–2020	2019
창시자	Multiple (Ratner et al. for weak supervision framework; Meng et al. for BERT integration)	Liu, Y. et al. (Facebook AI Research / University of Washington)
유형	Weakly supervised fine-tuning of pre-trained language model	Pre-trained transformer fine-tuned for sequence classification
원전	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.	Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.
별칭	WS-BERT, BERT with weak supervision, label-efficient BERT classification, noisy-label BERT fine-tuning	RoBERTa classifier, RoBERTa text classification, Robustly Optimized BERT Classification, RoBERTa fine-tuning for classification
관련	6	5
요약	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.	RoBERTa-based Classification applies the RoBERTa pre-trained transformer — trained more robustly than BERT with dynamic masking and larger batches — to text categorisation tasks by adding a lightweight classification head on top of the [CLS] token representation and fine-tuning the entire model on labelled examples. It consistently matches or outperforms BERT on standard NLP benchmarks.