Fetching the paper…
Reading the bibliography…
Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Glove: Global vectors for word//w representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 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
Earlier work this paper cites.
The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston · 2015
Earlier work this paper cites.
Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D. Manning · 2015
Earlier work this paper cites.
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun · 2015
Earlier work this paper cites.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Józefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Gordon Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
Earlier work this paper cites.
Lei Jimmy Ba, Ryan Kiros, and Geoffrey E. Hinton · 2016
Earlier work this paper cites.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 2016
Earlier work this paper cites.
Wikireading: A novel large-scale language understanding task over wikipedia
Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, and David Berthelot · 2016
Earlier work this paper cites.
Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q. Weinberger · 2016
Earlier work this paper cites.
Learning recurrent span representations for extractive question answering
Kenton Lee, Tom Kwiatkowski, Ankur P. Parikh, and Dipanjan Das · 2016
Cited alongside, same era.
Squad: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Cited alongside, same era.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
Cited alongside, same era.
Bidirectional attention flow for machine comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi · 2016
Cited alongside, same era.
Machine comprehension using match-lstm and answer pointer
Shuohang Wang and Jing Jiang · 2016
Cited alongside, same era.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
Later among the works it cites.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer · 2017
Later among the works it cites.
Depthwise separable convolutions for neural machine translation
Lukasz Kaiser, Aidan N Gomez, and Francois Chollet · 2017
Later among the works it cites.
Structural embedding of syntactic trees for machine comprehension
Rui Liu, Junjie Hu, Wei Wei, Zi Yang, and Eric Nyberg · 2017
Later among the works it cites.
Neural machine translation (seq2seq) tutorial
Minh-Thang Luong, Eugene Brevdo, and Rui Zhao · 2017
Later among the works it cites.
Paraphrasing revisited with neural machine translation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Multi-perspective context matching for machine comprehension
Zhiguo Wang, Haitao Mi, Wael Hamza, and Radu Florian · 2016
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016
Cited alongside, same era.
Dynamic coattention networks for question answering
Caiming Xiong, Victor Zhong, and Richard Socher · 2016
Cited alongside, same era.
End-to-end reading comprehension with dynamic answer chunk ranking
Yang Yu, Wei Zhang, Kazi Saidul Hasan, Mo Yu, Bing Xiang, and Bowen Zhou · 2016
Cited alongside, same era.
Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes · 2017
Cited alongside, same era.
Simple and effective multi-paragraph reading comprehension
Christopher Clark and Matt Gardner · 2017
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 · 2017
Cited alongside, same era.
Jonathan Mallinson, Rico Sennrich, and Mirella Lapata · 2017
Later among the works it cites.
MEMEN: multi-layer embedding with memory networks for machine comprehension
Boyuan Pan, Hao Li, Zhou Zhao, Bin Cao, Deng Cai, and Xiaofei He · 2017
Later among the works it cites.
Globally normalized reader
Jonathan Raiman and John Miller · 2017
Later among the works it cites.
Reasonet: Learning to stop reading in machine comprehension
Yelong Shen, Po-Sen Huang, Jianfeng Gao, and Weizhu Chen · 2017
Later among the works it cites.
Gated self-matching networks for reading comprehension and question answering
Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou · 2017
Later among the works it cites.
Making neural QA as simple as possible but not simpler
Dirk Weissenborn, Georg Wiese, and Laura Seiffe · 2017
Later among the works it cites.
Learning paraphrastic sentence embeddings from back-translated bitext
John Wieting, Jonathan Mallinson, and Kevin Gimpel · 2017
Later among the works it cites.
Learning to skim text
Adams Wei Yu, Hongrae Lee, and Quoc V. Le · 2017
Later among the works it cites.
Exploring question understanding and adaptation in neural-network-based question answering
Junbei Zhang, Xiao-Dan Zhu, Qian Chen, Li-Rong Dai, Si Wei, and Hui Jiang · 2017
Later among the works it cites.
Neural question generation from text: A preliminary study
Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, and Ming Zhou · 2017
Later among the works it cites.