Fetching the paper…
Reading the bibliography…
Several deep learning models have been proposed for question answering.
On the statistical analysis of dirty pictures
Julian Besag · 1986
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Maxout networks
Ian J Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C Courville, and Yoshua Bengio · 2013
Earlier work this paper cites.
Mctest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher JC Burges, and Erin Renshaw · 2013
Earlier work this paper cites.
Modeling biological processes for reading comprehension
Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Abby Vander Linden, Brittany Harding, Brad Huang, Peter Clark, and Christopher D Manning · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
The stanford corenlp natural language processing toolkit
Christopher D Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
Cited alongside, same era.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
Cited alongside, same era.
Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D. Manning · 2015
Cited alongside, same era.
Training very deep networks
Rupesh K Srivastava, Klaus Greff, and Juergen Schmidhuber · 2015
Cited alongside, same era.
Chainer: a next-generation open source framework for deep learning
Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton · 2015
Cited alongside, same era.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
Cited alongside, same era.
The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston · 2016
Closest in time.
Text understanding with the attention sum reader network
Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst · 2016
Closest in time.
Hierarchical question-image co-attention for visual question answering
Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh · 2016
Closest in time.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
Closest in time.
Squad: 100,000+ questions for machine comprehension of text
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang · 2016
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Machine comprehension with syntax, frames, and semantics
Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester · 2015
Cited alongside, same era.
A thorough examination of the cnn/daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D. Manning · 2016
Cited alongside, same era.
Attention-over-attention neural networks for reading comprehension
Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, and Guoping Hu · 2016
Cited alongside, same era.
Machine comprehension using match-LSTM and answer pointer
Shuohang Wang and Jing Jiang
Cited in the paper.
Iterative alternating neural attention for machine reading
Alessandro Sordoni, Phillip Bachman, and Yoshua Bengio · 2016
Closest in time.
Learning natural language inference with LSTM
Shuohang Wang and Jing Jiang · 2016
Closest in time.
End-to-End Reading Comprehension with Dynamic Answer Chunk Ranking
Y. Yu, W. Zhang, K. Hasan, M. Yu, B. Xiang, and B. Zhou · 2016
Closest in time.
End-to-end answer chunk extraction and ranking for reading comprehension
Yang Yu, Wei Zhang, Kazi Hasan, Mo Yu, Bing Xiang, and Bowen Zhou · 2016
Closest in time.