論文略讀 | Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base
來源： 2018 NIPS–微軟亞洲研究院 參考：https://www.msra.cn/zh-cn/news/features/nips-2018-papers-from-msra 1. 文章概述： 對話中的多輪問答和語義分析是NLP領域兩個核心問題，對語音助手、聊天機器人和搜索引擎都至關重要。在本文中，我們提出了基於知識圖譜的對話式語義分析模型。（有點沒想通複製了之前的KB實體之後呢?就是複製
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相關文章
- 1. 論文解讀：Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Info...
- 2. 論文解讀：Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
- 3. 論文略讀 | Pattern-revising Enhanced Simple Question Answering over Knowledge Bases
- 4. Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
- 5. 論文解讀：Improved Neural Relation Detection for Knowledge Base Question Answering
- 6. A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs 論文解讀
- 7. Knowledge Base Question Answering via Encodin of Complex Query Graphs
- 8. Improved Neural Relation Detection for Knowledge Base Question Answering
- 9. An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global
- 10. 論文淺嘗 | Question Answering over Freebase