So sánh phương pháp

	Hỏi đáp bán giám sát	Hỏi đáp yếu giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2006–2020	2017–2019
Người khởi xướng	Multiple (Chapelle et al.; Zhu; Clark et al. for NLP applications)	Multiple authors (Clark, Gardner, Min et al.)
Loại	Semi-supervised learning applied to extractive/generative QA	Weakly supervised NLP model
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.	Clark, C., & Gardner, M. (2018). Simple and Effective Multi-Paragraph Reading Comprehension. In Proceedings of ACL 2018, pp. 845–855. Association for Computational Linguistics.
Tên gọi khác	Semi-supervised QA, Self-training for QA, Pseudo-labeled Question Answering, SSL-QA	WS-QA, distantly supervised QA, noisy-label question answering, indirect supervision QA
Liên quan	6	4
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.	Weakly supervised question answering (WS-QA) trains neural reading-comprehension models using indirect or automatically derived answer labels rather than expensive human-annotated span annotations. By exploiting distant supervision, heuristic labeling, or answer-presence signals, WS-QA makes QA feasible in domains and languages where full annotation is impractical.