Порівняння методів

	Слабо контрольоване запитання-відповідь	Адаптація до домену для відповідей на запитання
Галузь	Глибоке навчання	Глибоке навчання
Родина	Machine learning	Machine learning
Рік появи	2017–2019	2019–2020
Автор методу	Multiple authors (Clark, Gardner, Min et al.)	Multiple (e.g., Garg et al.; Yue et al.)
Тип	Weakly supervised NLP model	Domain adaptation for extractive/generative QA
Основоположне джерело	Clark, C., & Gardner, M. (2018). Simple and Effective Multi-Paragraph Reading Comprehension. In Proceedings of ACL 2018, pp. 845–855. Association for Computational Linguistics.	Garg, S., Vu, T., & Moschitti, A. (2020). TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(5), 7780–7788.
Інші назви	WS-QA, distantly supervised QA, noisy-label question answering, indirect supervision QA	DA-QA, domain-adapted QA, domain-specific question answering, cross-domain question answering
Пов'язані	4	6
Підсумок	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.	Domain-adaptive Question Answering (DA-QA) adapts a pre-trained language model — typically BERT or RoBERTa — first trained on general QA benchmarks such as SQuAD to answer questions accurately in a new target domain (e.g., biomedical, legal, financial) where labelled data is scarce. Combining domain-adaptive pre-training with task fine-tuning yields substantially stronger performance than direct fine-tuning alone.