مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| پرسش و پاسخ با نظارت ضعیف× | پرسش و پاسخ سازگار با دامنه (DA-QA)× | |
|---|---|---|
| حوزه | یادگیری عمیق | یادگیری عمیق |
| خانواده | Machine learning | Machine learning |
| سال پیدایش≠ | 2017–2019 | 2019–2020 |
| پدیدآور≠ | Multiple authors (Clark, Gardner, Min et al.) | Multiple (e.g., Garg et al.; Yue et al.) |
| نوع≠ | Weakly supervised NLP model | Domain adaptation for extractive/generative QA |
| منبع بنیادین≠ | Clark, C., & Gardner, M. (2018). Simple and Effective Multi-Paragraph Reading Comprehension. In Proceedings of ACL 2018, pp. 845–855. Association for Computational Linguistics. link ↗ | 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. DOI ↗ |
| نامهای دیگر | 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 |
| مرتبط≠ | 4 | 6 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
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