So sánh phương pháp

	Hỏi đáp bán giám sát	Phân loại dựa trên BERT bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2006–2020	2019–2020
Người khởi xướng	Multiple (Chapelle et al.; Zhu; Clark et al. for NLP applications)	Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base)
Loại	Semi-supervised learning applied to extractive/generative QA	Semi-supervised fine-tuning of pre-trained transformer
Công trình gốc	Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020). ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. In Proceedings of ICLR 2020.	Xie, Q., Dai, Z., Hovy, E., Luong, T., & Le, Q. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 27780–27792.
Tên gọi khác	Semi-supervised QA, Self-training for QA, Pseudo-labeled Question Answering, SSL-QA	Semi-supervised BERT, BERT SSL Classification, BERT with Unlabeled Data, BERT Semi-supervised Fine-tuning
Liên quan	6	6
Tóm tắt	Semi-supervised question answering (QA) trains a model on a small labeled set of question-answer pairs, then generates pseudo-labels on a large unlabeled corpus and retrains iteratively. This self-training loop dramatically increases effective training data without the cost of full manual annotation, achieving strong performance on reading comprehension, open-domain QA, and machine reading tasks.	Semi-supervised BERT-based classification fine-tunes a pre-trained BERT encoder on a small pool of labeled text examples while simultaneously leveraging a much larger body of unlabeled text — via consistency training, pseudo-labeling, or data augmentation — to produce high-quality classifiers even when manual annotation is scarce.