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In recent years, there have been amazing advances in deep learning methods for machine reading.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Data-intensive question answering
Eric Brill, Jimmy J. Lin, Michele Banko, Susan T. Dumais, and Andrew Y. Ng. 2001 · 2001
Earlier work this paper cites.
Learning to rank with (a lot of) word features
Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Olivier Chapelle, and Kilian Q. Weinberger. 2010 · 2010
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
Knowledge base completion via search-based question answering
Robert West, Evgeniy Gabrilovich, Kevin Murphy, Shaohua Sun, Rahul Gupta, and Dekang Lin. 2014 · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Cited alongside, same era.
Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan L. Boyd-Graber, and Hal Daumé III. 2015 · 2015
Cited alongside, same era.
Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Cited alongside, same era.
Squad: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
Bidirectional attention flow for machine comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
Cited alongside, same era.
Gated-attention readers for text comprehension
Bhuwan Dhingra, Hanxiao Liu, Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. 2017a · 2017
Later among the works it cites.
A deep relevance matching model for ad-hoc retrieval
Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2017 · 2017
Later among the works it cites.
A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Tim Lillicrap. 2017 · 2017
Later among the works it cites.
R$ˆ3$: Reinforced reader-ranker for open-domain question answering
Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerald Tesauro, Bowen Zhou, and Jing Jiang. 2017 · 2017
Later among the works it cites.
A broad-coverage challenge corpus for sentence understanding through inference
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Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
Cited alongside, same era.
Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
Cited alongside, same era.
Quasar: Datasets for question answering by search and reading
Bhuwan Dhingra, Kathryn Mazaitis, and William W Cohen. 2017b
Cited in the paper.
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2017 · 2017
Later among the works it cites.
Natural language inference by tree-based convolution and heuristic matching
Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, and Zhi Jin. 2016 · 2022
Closest in time.