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	Półnadzorowane odpowiadanie na pytania	Samonadzorowane odpowiadanie na pytania
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2006–2020	2019
Twórca	Multiple (Chapelle et al.; Zhu; Clark et al. for NLP applications)	Lewis, P.; Alberti, C. et al. (multiple independent groups ~2019)
Typ	Semi-supervised learning applied to extractive/generative QA	Self-supervised NLP training paradigm
Źródło pierwotne	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.	Lewis, P., Denoyer, L., & Riedel, S. (2019). Unsupervised Question Answering by Cloze Translation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pp. 4896–4910.
Inne nazwy	Semi-supervised QA, Self-training for QA, Pseudo-labeled Question Answering, SSL-QA	SSQA, unsupervised question answering, self-supervised QA, zero-label question answering
Pokrewne	6	1
Podsumowanie	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.	Self-supervised Question Answering (SSQA) is a training paradigm that automatically generates question-answer pairs from unlabeled text — using cloze translation, span masking, or neural question generation — to train QA models without any human-labeled data. It enables high-quality reading comprehension systems even when annotated datasets are scarce or domain-specific.