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Recurrent neural networks (RNNs) can model natural language by sequentially 'reading' input tokens and outputting a distributed representation of each token.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 1904
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Actor-critic algorithms
Vijay R Konda and John N Tsitsiklis · 2000
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee · 2005
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Part of speech n-grams and information retrieval
Christina Lioma and CJ Keith van Rijsbergen · 2008
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Learning word vectors for sentiment analysis
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Recurrent models of visual attention
Volodymyr Mnih, Nicolas Heess, Alex Graves, et al · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al · 2015
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Long short-term memory-networks for machine reading
Jianpeng Cheng, Li Dong, and Mirella Lapata · 2016
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The goldilocks principle: Reading children’s books with explicit memory representations
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Coarse-to-fine question answering for long documents
Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, and Jonathan Berant · 2017
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Length adaptive recurrent model for text classification
Zhengjie Huang, Zi Ye, Shuangyin Li, and Rong Pan · 2017
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Learning when to skim and when to read
Alexander Johansen and Richard Socher · 2017
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Learning to skim text
Adams Wei Yu, Hongrae Lee, and Quoc Le · 2017
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Skip rnn: Learning to skip state updates in recurrent neural networks
Víctor Campos, Brendan Jou, Xavier Giró-i Nieto, Jordi Torres, and Shih-Fu Chang · 2018
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Speed reading: Learning to read forbackward via shuttle
Tsu-Jui Fu and Wei-Yun Ma · 2018
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Asynchronous methods for deep reinforcement learning
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Phased lstm: Accelerating recurrent network training for long or event-based sequences
Daniel Neil, Michael Pfeiffer, and Shih-Chii Liu · 2016
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Neural speed reading via skim-rnn
Minjoon Seo, Sewon Min, Ali Farhadi, and Hannaneh Hajishirzi · 2018
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Fast and accurate text classification: Skimming, rereading and early stopping, 2018
Keyi Yu, Yang Liu, Alexander G. Schwing, and Jian Peng · 2018
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