Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering筆記
來源: COLING 2018 Long Paper 原文 Movation 以往的模型在複雜問題（問題實體和答案實體之間相隔較遠）上的表現很差。 作者認爲： We claim that one needs to explicitly model the semantic structure to be able to find the correct semantic parse for com
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相關文章
- 1. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering讀書筆記
- 2. Improved Neural Relation Detection for Knowledge Base Question Answering
- 3. Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base（筆記）
- 4. 【PaperReading】Knowledge Graph Embedding Based Question Answering
- 5. 論文解讀：Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Info...
- 6. Neural Generative Question Answering筆記
- 7. 論文解讀：Improved Neural Relation Detection for Knowledge Base Question Answering
- 8. Gated Self-Matching Networks for Reading Comprehension and Question Answering論文閱讀筆記
- 9. 【閱讀筆記】Reasoning With Neural Tensor Networks for Knowledge Base Completion
- 10. Question Answering by Reasoning Across Documents with Graph Convolutional Networks