Fetching the paper…
Reading the bibliography…
Reading a document and extracting an answer to a question about its content has attracted substantial attention recently.
Long short-term memory
S. Hochreiter and J. Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Bidirectional recurrent neural networks
Mike Schuster and Kuldip K. Paliwal. 1997 · 1997
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E. Hinton. 2010 · 2010
Earlier work this paper cites.
One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, and Phillipp Koehn. 2013 · 2013
Earlier work this paper cites.
Semi-supervised learning and domain adaptation for nlp
Anders Søgaard. 2013 · 2013
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim. 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.
Long short-term memory recurrent neural network architectures for large scale acoustic modeling
Hasim Sak, Andrew W. Senior, and Françoise Beaufays. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Semi-supervised sequence learning
Andrew M. Dai and Quoc V. Le. 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
Earlier work this paper cites.
Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan L. Boyd-Graber, and Hal Daumé III. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Lei Ba. 2015 · 2015
Earlier work this paper cites.
Ask me anything: Dynamic memory networks for natural language processing
Ankit Kumar, Ozan Irsoy, Jonathan Su, James Bradbury, Robert English, Brian Pierce, Peter Ondruska, Ishaan Gulrajani, and Richard Socher. 2015 · 2015
Earlier work this paper cites.
Highway networks
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015 · 2015
Cited alongside, same era.
End-to-end memory networks
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. 2015 · 2015
Cited alongside, same era.
Towards ai-complete question answering: A set of prerequisite toy tasks
Jason Weston, Antoine Bordes, Sumit Chopra, and Tomas Mikolov. 2015 · 2015
Cited alongside, same era.
Lstm: A search space odyssey
Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, and Jurgen Schmidhuber. 2016 · 2016
Cited alongside, same era.
Exploring the limits of language modeling
Rafal Józefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. 2016 · 2016
Cited alongside, same era.
Learning recurrent span representations for extractive question answering
Attention-over-attention neural networks for reading comprehension
Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, and Guoping Hu. 2017 · 2017
Closest in time.
Mnemonic reader for machine comprehension
Minghao Hu, Yuxing Peng, and Xipeng Qiu. 2017 · 2017
Closest in time.
Fusionnet: Fusing via fully-aware attention with application to machine comprehension
Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen, and Weizhu Chen. 2017 · 2017
Closest in time.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer. 2017 · 2017
Closest in time.
Residual LSTM: design of a deep recurrent architecture for distant speech recognition
Jaeyoung Kim, Mostafa El-Khamy, and Jungwon Lee. 2017 · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kenton Lee, Shimi Salant, Tom Kwiatkowski, Ankur P. Parikh, Dipanjan Das, and Jonathan Berant. 2016 · 2016
Cited alongside, same era.
MS MARCO: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Cited alongside, same era.
A decomposable attention model for natural language inference
Ankur P. Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. 2016 · 2016
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.
Newsqa: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2016 · 2016
Cited alongside, same era.
Multi-perspective context matching for machine comprehension
Zhiguo Wang, Haitao Mi, Wael Hamza, and Radu Florian. 2016 · 2016
Cited alongside, same era.
Closest in time.
Learned in translation: Contextualized word vectors
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017 · 2017
Closest in time.
Semi-supervised sequence tagging with bidirectional language models
Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power. 2017 · 2017
Closest in time.
Unsupervised pretraining for sequence to sequence learning
Prajit Ramachandran, Peter J. Liu, and Quoc V. Le. 2017 · 2017
Closest in time.
Reasonet: Learning to stop reading in machine comprehension
Yelong Shen, Po-Sen Huang, Jianfeng Gao, and Weizhu Chen. 2017 · 2017
Closest in time.
Gated self-matching networks for reading comprehension and question answering
Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou. 2017 · 2017
Closest in time.
Making neural QA as simple as possible but not simpler
Dirk Weissenborn, Georg Wiese, and Laura Seiffe. 2017 · 2017
Closest in time.
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 · 2017
Closest in time.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Closest in time.
Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2021
Closest in time.