論文分享 - Machine Comprehension Using Match-LSTM and Answer Pointe
介紹 在Machine Comprehension（MC）任務中，早期數據庫規模小，主要使用pipeline的方法；後來隨着深度學習的發展，2016年，一個比較大規模的數據庫出現了，即SQuAD。該文是第一個在SQuAD數據庫上測試的端到端神經網絡模型。主要結構包括兩部分：Match-LSTM和Pointer-Net，並針對Pointer-Net設計了兩種使用方法，序列模型（Sequence Mo
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相關文章
- 1. 論文分享 - Machine Comprehension Using Match-LSTM and Answer Pointer
- 2. MACHINE COMPREHENSION USING MATCH-LSTM AND ANSWER POINTER(MATCH-LSTM)
- 3. machine comprehension using match-lstm and answer pointer
- 4. [ICLR2017] Machine Comprehension Using Match-LSTM and Answer Pointer
- 5. Match-LSTM閱讀理解論文筆記Machine Comprehension Using Match-LSTM and Answer Pointer
- 6. 論文筆記--From Answer Extraction to Answer Generation for Machine Reading Comprehension (S-Net)
- 7. 論文分享 - Reinforced Mnemonic Reader for Machine Comprehension
- 8. 論文分享 - R-Net: Machine Reading Comprehension with Self-Matching
- 9. 論文筆記--Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (V-Net)
- 10. 【論文翻譯+筆記】Neural Machine Reading Comprehension: Methods and Trends