Gated Self-Matching Networks for Reading Comprehension and Question Answering論文筆記
原文下載鏈接 摘要 檢索式問答系統試圖從文檔中獲取問題的答案。一般步驟是先從一衆文檔中檢索相關文檔，然後再進一步檢索文檔回答問題。本文解決的是後一步，即閱讀理解式的問答系統。文章基於端到端的多層神經網絡模型從篇章中獲取答案。 模型分爲四部分：一是使用多層雙向神經網絡編碼問題和篇章的語義向量表示；二是使用門注意力機制得到問題感知的篇章的語義向量表示；三是通過 Self-Matching 注意力機制提
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相關文章
- 1. Gated Self-Matching Networks for Reading Comprehension and Question Answering論文閱讀筆記
- 2. Reading Note: Gated Self-Matching Networks for Reading Comprehension and Question Answering
- 3. 論文筆記 Dynamic Memory Networks for Visual and Textual Question Answering（ICML2016)
- 4. 論文《Adversarial Reading Networks For Machine Comprehension》
- 5. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering筆記
- 6. 論文筆記《Gated-Attention Readers for Text Comprehension》
- 7. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering讀書筆記
- 8. Attention-over-Attention Neural Networks for Reading Comprehension論文筆記
- 9. Learning to Paraphrase for Question Answering 論文筆記
- 10. Multimodal Encoder-Decoder Attention Networks for Visual Question Answering論文筆記