Machine learningDeep learning / NLP / CV
可解释问答
可解释问答（Explainable Question Answering, XQA）将神经阅读理解模型——通常是BERT系列变换器（transformers）——与可解释性方法（如理由提取、注意力可视化、LIME或SHAP）相结合，以揭示模型为何选择特定答案片段。其目标不仅是准确性，更是可信赖、可审计的推理过程，供用户和领域专家检查与验证。
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来源
- DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020). ERASER: A Benchmark to Evaluate Rationalized NLP Models. In Proceedings of ACL 2020, pp. 4443–4458. DOI: 10.18653/v1/2020.acl-main.408 ↗
- Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of EMNLP 2016, pp. 2383–2392. DOI: 10.18653/v1/D16-1264 ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Question Answering (XQA). ScholarGate. https://scholargate.app/zh/deep-learning/explainable-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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