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The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
Building a large annotated corpus of english: The penn treebank
Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini · 1993
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001
Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, and Jürgen Schmidhuber · 2001
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Effective self-training for parsing
David McClosky, Eugene Charniak, and Mark Johnson · 2006
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Learning accurate, compact, and interpretable tree annotation
Slav Petrov, Leon Barrett, Romain Thibaux, and Dan Klein · 2006
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Self-training PCFG grammars with latent annotations across languages
Zhongqiang Huang and Mary Harper · 2009
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Generating sequences with recurrent neural networks
Alex Graves · 2013
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Fast and accurate shift-reduce constituent parsing
Muhua Zhu, Yue Zhang, Wenliang Chen, Min Zhang, and Jingbo Zhu · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc VV Le · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Multi-task sequence to sequence learning
Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2015
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End-to-end memory networks
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus · 2015
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2015
Neural GPUs learn algorithms
Łukasz Kaiser and Ilya Sutskever · 2016
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A decomposable attention model
Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit · 2016
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Using the output embedding to improve language models
Ofir Press and Lior Wolf · 2016
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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, et al · 2016
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Deep recurrent models with fast-forward connections for neural machine translation
Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu · 2016
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Grammar as a foreign language
Vinyals & Kaiser, Koo, Petrov, Sutskever, and Hinton · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Long short-term memory-networks for machine reading
Jianpeng Cheng, Li Dong, and Mirella Lapata · 2016
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Xception: Deep learning with depthwise separable convolutions
Francois Chollet · 2016
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Recurrent neural network grammars
Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, and Noah A. Smith · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Exploring the limits of language modeling
Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu · 2016
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Massive exploration of neural machine translation architectures
Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc V. Le · 2017
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Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin · 2017
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Neural machine translation in linear time
Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, and Koray Kavukcuoglu · 2017
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Structured attention networks
Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush · 2017
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Factorization tricks for LSTM networks
Oleksii Kuchaiev and Boris Ginsburg · 2017
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A structured self-attentive sentence embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio · 2017
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A deep reinforced model for abstractive summarization
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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