论文分享 - Machine Comprehension Using Match-LSTM and Answer Pointe
介绍 在Machine Comprehension（MC）任务中，早期数据库规模小，主要使用pipeline的方法；后来随着深度学习的发展，2016年，一个比较大规模的数据库出现了，即SQuAD。该文是第一个在SQuAD数据库上测试的端到端神经网络模型。主要结构包括两部分：Match-LSTM和Pointer-Net，并针对Pointer-Net设计了两种使用方法，序列模型（Sequence Mo
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相关文章
- 1. 论文分享 - 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阅读理解论文笔记Machine Comprehension Using Match-LSTM and Answer Pointer
- 6. 论文笔记--From Answer Extraction to Answer Generation for Machine Reading Comprehension (S-Net)
- 7. 论文分享 - Reinforced Mnemonic Reader for Machine Comprehension
- 8. 论文分享 - R-Net: Machine Reading Comprehension with Self-Matching
- 9. 论文笔记--Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (V-Net)
- 10. 【论文翻译+笔记】Neural Machine Reading Comprehension: Methods and Trends