Gated Self-Matching Networks for Reading Comprehension and Question Answering论文笔记
原文下载链接 摘要 检索式问答系统试图从文档中获取问题的答案。一般步骤是先从一众文档中检索相关文档，然后再进一步检索文档回答问题。本文解决的是后一步，即阅读理解式的问答系统。文章基于端到端的多层神经网络模型从篇章中获取答案。 模型分为四部分：一是使用多层双向神经网络编码问题和篇章的语义向量表示；二是使用门注意力机制得到问题感知的篇章的语义向量表示；三是通过 Self-Matching 注意力机制提
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相关文章
- 1. Gated Self-Matching Networks for Reading Comprehension and Question Answering论文阅读笔记
- 2. Reading Note: Gated Self-Matching Networks for Reading Comprehension and Question Answering
- 3. 论文笔记 Dynamic Memory Networks for Visual and Textual Question Answering（ICML2016)
- 4. 论文《Adversarial Reading Networks For Machine Comprehension》
- 5. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering笔记
- 6. 论文笔记《Gated-Attention Readers for Text Comprehension》
- 7. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering读书笔记
- 8. Attention-over-Attention Neural Networks for Reading Comprehension论文笔记
- 9. Learning to Paraphrase for Question Answering 论文笔记
- 10. Multimodal Encoder-Decoder Attention Networks for Visual Question Answering论文笔记