เปรียบเทียบวิธี

	การตอบคำถามแบบมีผู้สอนแบบอ่อน (Weakly Supervised Question Answering)	การตอบคำถามแบบปรับตามโดเมน
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2017–2019	2019–2020
ผู้ริเริ่ม	Multiple authors (Clark, Gardner, Min et al.)	Multiple (e.g., Garg et al.; Yue et al.)
ประเภท	Weakly supervised NLP model	Domain adaptation for extractive/generative QA
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	4	6
สรุป	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.