論文分享 - R-Net: Machine Reading Comprehension with Self-Matching
介紹 該文由MSRA發表，在SQuAD數據庫上目前成績最好。模型借鑑了Wang&Jiang最早的match-LSTM方法，做了一些改進，網絡結構分爲以下四部分： RNN網絡分別對question和passage單獨編碼 基於門限的注意力循環神經網絡（gated-attention based recurrent network）匹配question和passage，獲取問題的相關段落表示（ques
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相關文章
- 1. 【論文筆記】Improving Machine Reading Comprehension with General Reading Strategies（2019，NAACL）
- 2. 論文閱讀|Cross-Lingual Machine Reading Comprehension
- 3. 論文《Adversarial Reading Networks For Machine Comprehension》
- 4. Read + Verify: Machine Reading Comprehension with Unanswerable Questions 論文閱讀筆記
- 5. 論文筆記--Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (V-Net)
- 6. 【論文筆記】Read + Verify: Machine Reading Comprehension with Unanswerable Questions
- 7. 論文分享 - Reinforced Mnemonic Reader for Machine Comprehension
- 8. 論文分享-->Attention-over-Attention Neural Networks for Reading Comprehension
- 9. 【論文筆記03】ReasoNet: Learning to Stop Reading in Machine Comprehension
- 10. 【論文翻譯+筆記】Neural Machine Reading Comprehension: Methods and Trends