论文分享 - R-Net: Machine Reading Comprehension with Self-Matching
介绍 该文由MSRA发表，在SQuAD数据库上目前成绩最好。模型借鉴了Wang&Jiang最早的match-LSTM方法，做了一些改进，网络结构分为以下四部分： RNN网络分别对question和passage单独编码 基于门限的注意力循环神经网络（gated-attention based recurrent network）匹配question和passage，获取问题的相关段落表示（ques
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相关文章
- 1. 【论文笔记】Improving Machine Reading Comprehension with General Reading Strategies（2019，NAACL）
- 2. 论文阅读|Cross-Lingual Machine Reading Comprehension
- 3. 论文《Adversarial Reading Networks For Machine Comprehension》
- 4. Read + Verify: Machine Reading Comprehension with Unanswerable Questions 论文阅读笔记
- 5. 论文笔记--Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (V-Net)
- 6. 【论文笔记】Read + Verify: Machine Reading Comprehension with Unanswerable Questions
- 7. 论文分享 - Reinforced Mnemonic Reader for Machine Comprehension
- 8. 论文分享-->Attention-over-Attention Neural Networks for Reading Comprehension
- 9. 【论文笔记03】ReasoNet: Learning to Stop Reading in Machine Comprehension
- 10. 【论文翻译+笔记】Neural Machine Reading Comprehension: Methods and Trends