论文分享 - Machine Comprehension Using Match-LSTM and Answer Pointer

时间 2020-12-24

标签问答系统 Machine Comprehension LSTM Attention NLP 栏目 Java开源繁體版

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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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1. MACHINE COMPREHENSION USING MATCH-LSTM AND ANSWER POINTER(MATCH-LSTM)
2. machine comprehension using match-lstm and answer pointer
3. [ICLR2017] Machine Comprehension Using Match-LSTM and Answer Pointer
4. 论文分享 - Machine Comprehension Using Match-LSTM and Answer Pointe
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

>>更多相关文章<<