Compară metode

	Clasificare semi-supervizată bazată pe RoBERTa	Clasificare RoBERTa bazată pe supervizare slabă
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2019–2020	2019–2020
Autorul original	Liu et al. (RoBERTa, 2019); semi-supervised adaptation by the NLP community	Liu et al. (RoBERTa, 2019); weak supervision paradigm: Ratner et al. (2016–2020)
Tip	Semi-supervised fine-tuning of a pretrained language model	Pretrained transformer classifier with weak supervision
Sursa seminală	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.	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:1907.11692.
Denumiri alternative	Semi-supervised RoBERTa, RoBERTa with semi-supervised learning, SSL-RoBERTa classification, RoBERTa pseudo-label classification	WS-RoBERTa, RoBERTa with weak supervision, weakly supervised transformer classification, noisy-label RoBERTa classifier
Înrudite	6	5
Rezumat	Semi-supervised RoBERTa-based classification combines a large pretrained RoBERTa language model with both a small labeled dataset and a larger pool of unlabeled text. By generating pseudo-labels or enforcing consistency on unlabeled examples, the method extracts supervisory signal from unannotated data, yielding stronger classifiers when ground-truth annotations are scarce.	Weakly supervised RoBERTa-based classification combines the RoBERTa pretrained transformer with weak supervision — programmatic or heuristic labeling sources — to train powerful text classifiers without requiring a fully hand-labeled dataset. Labeling functions, distant supervision, or crowd-sourced signals generate noisy labels that are aggregated and used to fine-tune RoBERTa for downstream classification tasks.