论文笔记--Multi-Style Generative Reading Comprehension (Masque)
First published on indexfziq.github.io at 2019-04-10 17:00:00 Introduction 目前，本文是MARCO数据集NLG任务的第一名，作者是NTT公司的Nishina等人。文章突破了抽取式阅读理解模型的束缚，采用摘要的方式生成指定风格的答案，这两个突破算是GMRC的一个里程碑。在此篇文章之后，分析选手榜可以发现，沿用此方法的模型也都取
相关文章
- 1. NTT Masque: 多风格生成式阅读理解（Multi-Style Generative Reading Comprehension）
- 2. 【论文笔记】Improving Machine Reading Comprehension with General Reading Strategies（2019，NAACL）
- 3. 【论文翻译+笔记】Neural Machine Reading Comprehension: Methods and Trends
- 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. Gated Self-Matching Networks for Reading Comprehension and Question Answering论文笔记
- 7. Attention-over-Attention Neural Networks for Reading Comprehension论文笔记
- 8. 【论文笔记】Simple and Effective Multi-Paragraph Reading Comprehension
- 9. 【论文笔记03】ReasoNet: Learning to Stop Reading in Machine Comprehension
- 10. 【论文笔记】QANET：Combining Local Convolution With Global Self-attention for Reading Comprehension
- 更多相关文章...
- • ASP.NET Razor - 标记 - ASP.NET 教程
- • CAP理论是什么？ - NoSQL教程
- • Tomcat学习笔记（史上最全tomcat学习笔记）
- • Scala 中文乱码解决
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. NTT Masque: 多风格生成式阅读理解（Multi-Style Generative Reading Comprehension）
- 2. 【论文笔记】Improving Machine Reading Comprehension with General Reading Strategies（2019，NAACL）
- 3. 【论文翻译+笔记】Neural Machine Reading Comprehension: Methods and Trends
- 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. Gated Self-Matching Networks for Reading Comprehension and Question Answering论文笔记
- 7. Attention-over-Attention Neural Networks for Reading Comprehension论文笔记
- 8. 【论文笔记】Simple and Effective Multi-Paragraph Reading Comprehension
- 9. 【论文笔记03】ReasoNet: Learning to Stop Reading in Machine Comprehension
- 10. 【论文笔记】QANET：Combining Local Convolution With Global Self-attention for Reading Comprehension