Bandingkan metode

	Tanya Jawab yang Diawasi Secara Lemah	Tanya Jawab Adaptif Domain
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2017–2019	2019–2020
Pencetus	Multiple authors (Clark, Gardner, Min et al.)	Multiple (e.g., Garg et al.; Yue et al.)
Tipe	Weakly supervised NLP model	Domain adaptation for extractive/generative QA
Sumber perintis	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.
Alias	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
Terkait	4	6
Ringkasan	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.