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State-of-the-art results on neural machine translation often use attentional sequence-to-sequence models with some form of convolution or recursion.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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The vanishing gradient problem during learning recurrent neural nets and problem solutions
Sepp Hochreiter · 1998
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Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001
Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, Jürgen Schmidhuber, et al · 2001
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Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 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 Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 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 V Le · 2014
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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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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Quasi-recurrent neural networks
James Bradbury, Stephen Merity, Caiming Xiong, and Richard Socher · 2016
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A convolutional encoder model for neural machine translation
Jonas Gehring, Michael Auli, David Grangier, and Yann N Dauphin · 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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Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
Hakan Inan, Khashayar Khosravi, and Richard Socher · 2016
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Can active memory replace attention?
Łukasz Kaiser and Samy Bengio · 2016
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Deep architectures for neural machine translation
Antonio Valerio Miceli Barone, Jindřich Helcl, Rico Sennrich, Barry Haddow, and Alexandra Birch · 2017
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Xavier Gastaldi · 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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Yoon Kim, Carl Denton, Luong Hoang, and Alexander M Rush · 2017
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Tao Lei and Yu Zhang · 2017
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A decomposable attention model for natural language inference
Ankur P 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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Aggregated residual transformations for deep neural networks
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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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A structured self-attentive sentence embedding
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On the state of the art of evaluation in neural language models
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Regularizing and optimizing LSTM language models
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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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The Microsoft 2016 conversational speech recognition system
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