【专知荟萃22】机器阅读理解RC知识资料全集（入门/进阶/论文/综述/代码/专家，附查看）

• 机器阅读理解（Reading Comprehension）专知荟萃
  • 入门学习
  • 进阶论文
  • 综述
  • Datasets
  • Code
  • 领域专家

入门学习

1. 深度学习解决机器阅读理解任务的研究进展 张俊林
   • [https://zhuanlan.zhihu.com/p/22671467]
2. 从短句到长文，计算机如何学习阅读理解 微软亚洲研究院
   • [http://www.msra.cn/zh-cn/news/features/machine-text-comprehension-20170508]
3. 基于深度学习的阅读理解 冯岩松
   • [http://cips-upload.bj.bcebos.com/2017/ssatt2017/QA_2017_QAII.pdf\]
4. SQuAD综述
   • [https://www.jiqizhixin.com/articles/2017-05-21]
5. 教机器学习阅读 张俊
   • [https://baijia.baidu.com/s?old_id=507397\]
6. 解读DeepMind的论文“教会机器阅读和理解”
   • [http://www.jianshu.com/p/4da1dea4f541]
7. 机器阅读理解中文章和问题的深度学习表示方法
   • [http://blog.csdn.net/malefactor/article/details/52599733]

进阶论文

1. Teaching Machines to Read and Comprehend
   • [https://arxiv.org/abs/1506.03340]
2. Learning to Ask: Neural Question Generation for Reading Comprehension
   • https://arxiv.org/pdf/1705.00106.pdf
3. Attention-over-Attention Neural Networks for Reading Comprehension
   • https://arxiv.org/pdf/1607.04423.pdf
4. R-NET: MACHINE READING COMPREHENSION WITH SELF-MATCHING NETWORKS
   • https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf
5. Mnemonic Reader for Machine Comprehension
   • https://arxiv.org/pdf/1705.02798.pdf
6. TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
   • https://arxiv.org/pdf/1705.03551.pdf
7. S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension
   • https://arxiv.org/pdf/1706.04815.pdf
8. RACE: Large-scale ReAding Comprehension Dataset From Examinations
   • https://arxiv.org/pdf/1704.04683.pdf
9. Adversarial Examples for Evaluating Reading Comprehension Systems
   • https://arxiv.org/pdf/1707.07328.pdf
10. Machine comprehension using match-lstm and answer pointer
    • [https://arxiv.org/pdf/1608.07905]
11. Multi-perspective context matching for machine comprehension
    • [https://arxiv.org/abs/1612.04211]
12. Reasonet: Learning to stop reading in machine comprehension
    • [http://dl.acm.org/citation.cfm?id=3098177]
13. Learning recurrent span representations for extractive question answering
    • [https://arxiv.org/abs/1611.01436]
14. End-to-end answer chunk extraction and ranking for reading comprehension
    • [https://arxiv.org/abs/1610.09996]
15. Words or characters? fine-grained gating for reading comprehension
    • [https://arxiv.org/abs/1611.01724]
16. Reading Wikipedia to Answer Open-Domain Questions
    • [https://arxiv.org/abs/1704.00051]
17. An analysis of prerequisite skills for reading comprehension
    • [http://www.aclweb.org/anthology/W/W16/W16-60.pdf#page=13]
18. A Comparative Study of Word Embeddings for Reading Comprehension
    • https://arxiv.org/pdf/1703.00993.pdf

综述

1. Emergent Logical Structure in Vector Representations of Neural Readers
   • [https://arxiv.org/pdf/1611.07954v1.pdf]
2. 机器阅读理解任务综述 林鸿宇 韩先培
   • [https://mp.weixin.qq.com/s?__biz=MzIxNzE2MTM4OA==&mid=2665643130&idx=1&sn=5f75f0d4978289caea6c4cb37b0b74c4\]

Datasets

1. MCTest
   • http://research.microsoft.com/en-us/um/redmond/projects/mctest/data.html
2. bAbI
   • https://research.fb.com/projects/babi/
3. WikiQA
   • https://www.microsoft.com/en-us/download/details.aspx?id=52419
4. SNLI
   • http://nlp.stanford.edu/projects/snli/
5. Children's Book Test
   • https://research.fb.com/projects/babi/
6. BookTest
   • https://ibm.ent.box.com/v/booktest-v1
7. CNN / Daily Mail
   • http://cs.nyu.edu/~kcho/DMQA/
8. Who Did What
   • https://tticnlp.github.io/who_did_what/download.html
9. NewsQA
   • http://datasets.maluuba.com/NewsQA
10. SQuAD
    • https://rajpurkar.github.io/SQuAD-explorer/
11. LAMBADA
    • http://clic.cimec.unitn.it/lambada/
12. MS MARCO
    • http://www.msmarco.org/dataset.aspx
13. WikiMovies
    • https://research.fb.com/projects/babi/
14. WikiReading
    • https://github.com/dmorr-google/wiki-reading

Code

1. CNN/Daily Mail Reading Comprehension Task
   • [https://github.com/danqi/rc-cnn-dailymail]
2. TriviaQA
   • [https://github.com/mandarjoshi90/triviaqa]
3. Attentive Reader
   • [https://github.com/lhoang29/attentive-reader]
4. DrQA
   • [https://github.com/hitvoice/DrQA]

领域专家

1. Percy Liang
   • [https://cs.stanford.edu/~pliang/\]
2. 刘挺
   • [http://homepage.hit.edu.cn/liuting]
3. Jason Weston
   • [https://research.fb.com/people/weston-jason/]