方法对比
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| 弱监督问答× | 领域自适应问答× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017–2019 | 2019–2020 |
| 提出者≠ | Multiple authors (Clark, Gardner, Min et al.) | Multiple (e.g., Garg et al.; Yue et al.) |
| 类型≠ | Weakly supervised NLP model | Domain adaptation for extractive/generative QA |
| 开创性文献≠ | 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 ↗ |
| 别名 | 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 |
| 相关≠ | 4 | 6 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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