Machine learningDeep learning / NLP / CV
弱监督问答
弱监督问答（WS-QA）使用间接或自动派生的答案标签来训练神经阅读理解模型，而不是昂贵的人工标注跨度注释。通过利用远程监督、启发式标注或答案存在信号，WS-QA使得在完全标注不切实际的领域和语言中进行问答成为可能。
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Method map
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来源
- 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 ↗
- Min, S., Chen, D., Hajishirzi, H., & Zettlemoyer, L. (2019). A Discrete Hard EM Approach for Weakly Supervised Question Answering. In Proceedings of EMNLP-IJCNLP 2019, pp. 2083–2093. Association for Computational Linguistics. link ↗
如何引用本页
ScholarGate. (2026, June 3). Weakly Supervised Question Answering. ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-question-answering
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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