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【论文笔记】QANET:Combining Local Convolution With Global Self-attention for Reading Comprehension
时间 2020-12-24
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NLP
QANet
MRC
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目录 1. 简要介绍 2. 模型 3. data augmentation by backtranslation 4. 实验 1. 简要介绍 模型创新点: (一)移除了RNN,核心就是卷积 + self-attention。这样使得训练更快,相应地模型能使用更多的训练数据。Convolution capture the local structure of context(lo
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相关文章
1.
QANet: Combining Local Convolution With Global Self-Attention For Reading Comprehension
2.
QANET: COMBINING LOCAL CONVOLUTION WITH GLOBAL SELF-ATTENTION FOR READING COMPREHENSION
3.
【论文笔记】Improving Machine Reading Comprehension with General Reading Strategies(2019,NAACL)
4.
机器阅读理解系列文章-QANet(Combining Local Convolution with Global Self-Attention for Reading Comprehension)
5.
Attention-over-Attention Neural Networks for Reading Comprehension论文笔记
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论文笔记--Multi-Style Generative Reading Comprehension (Masque)
7.
Read + Verify: Machine Reading Comprehension with Unanswerable Questions 论文阅读笔记
8.
论文笔记--From Answer Extraction to Answer Generation for Machine Reading Comprehension (S-Net)
9.
论文笔记--Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (V-Net)
10.
【论文笔记】Read + Verify: Machine Reading Comprehension with Unanswerable Questions
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