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Recent advancements in attention mechanisms have replaced recurrent neural networks and its variants for machine translation tasks.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Learning accurate, compact, and interpretable tree annotation
Slav Petrov, Leon Barrett, Romain Thibaux, and Dan Klein · 2006
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Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus · 2013
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
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 Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 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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Attention-based models for speech recognition
Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, KyungHyun Cho, and Yoshua Bengio · 2015
Earlier work this paper cites.
Weakly supervised memory networks
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2015
Earlier work this paper cites.
Grammar as a foreign language
Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey Hinton · 2015
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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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Can active memory replace attention?
Ł ukasz Kaiser and Samy Bengio · 2016
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Neural machine translation in linear time
Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aäron van den Oord, Alex Graves, and Koray Kavukcuoglu · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2016
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Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Depthwise separable convolutions for neural machine translation
Lukasz Kaiser, Aidan N. Gomez, and François Chollet · 2017
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Exploiting linguistic resources for neural machine translation using multi-task learning
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Layer normalization, 2016
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 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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Using the output embedding to improve language models
Ofir Press and Lior Wolf · 2016
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, and Jeff Dean · 2017
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Jan Niehues and Eunah Cho · 2017
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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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Named-entity tagging and domain adaptation for better customized translation
Zhongwei Li, Xuancong Wang, Ai Ti Aw, Eng Siong Chng, and Haizhou Li · 2018
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Scaling neural machine translation
Myle Ott, Sergey Edunov, David Grangier, and Michael Auli · 2018
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Pay less attention with lightweight and dynamic convolutions
Felix Wu, Angela Fan, Alexei Baevski, Yann N. Dauphin, and Michael Auli · 2019
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Data diversification: An elegant strategy for neural machine translation, 2019
Xuan-Phi Nguyen, Shafiq Joty, Wu Kui, and Ai Ti Aw · 2019
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Muse: Parallel multi-scale attention for sequence to sequence learning, 2019
Guangxiang Zhao, Xu Sun, Jingjing Xu, Zhiyuan Zhang, and Liangchen Luo · 2019
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