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The recently proposed BERT has shown great power on a variety of natural language understanding tasks, such as text classification, reading comprehension, etc.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le · 1906
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Towards making the most of bert in neural machine translation
Jiacheng Yang, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Yong Yu, Weinan Zhang, and Lei Li · 1908
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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The difficulty of training deep architectures and the effect of unsupervised pre-training
Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio, and Pascal Vincent · 2009
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Why does unsupervised pre-training help deep learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Report on the 11th iwslt evaluation campaign, iwslt 2014
Mauro Cettolo, Jan Niehues, Sebastian Stüker, Luisa Bentivogli, and Marcello Federico · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 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
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Semi-supervised sequence learning
Andrew M Dai and Quoc V Le · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin · 2017
Cited alongside, same era.
Fractalnet: Ultra-deep neural networks without residuals
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Learning to teach with dynamic loss functions
Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Lai Jian-Huang, and Tie-Yan Liu · 2018
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Findings of the 2019 conference on machine translation (wmt19)
Loïc Barrault, Ondr̆ej Bojar, Marta R. Costa-jussá, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias Müller, Santanu Pal, Matt Post, and Marcos Zampieri · 2019
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Distilling the knowledge of bert for text generation
Yen-Chun Chen, Zhe Gan, Yu Cheng, Jingzhou Liu, and Jingjing Liu · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Cross-lingual language model pretraining
Guillaume Lample and Alexis Conneau · 2019
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Latent alignment and variational attention
Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, and Alexander Rush · 2018
Cited alongside, same era.
Classical structured prediction losses for sequence to sequence learning
Sergey Edunov, Myle Ott, Michael Auli, David Grangier, and Marcaurelio Ranzato · 2018
Cited alongside, same era.
Ms-uedin submission to the wmt2018 ape shared task: Dual-source transformer for automatic post-editing
Marcin Junczys-Dowmunt and Roman Grundkiewicz · 2018
Cited alongside, same era.
Phrase-based & neural unsupervised machine translation
Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato · 2018
Cited alongside, same era.
Document-level neural machine translation with hierarchical attention networks
Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, and James Henderson · 2018
Cited alongside, same era.
Scaling neural machine translation
Myle Ott, Sergey Edunov, David Grangier, and Michael Auli · 2018
Cited alongside, same era.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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The evolved transformer
David So, Quoc Le, and Chen Liang · 2019
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MASS: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu · 2019
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Multi-agent dual learning
Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu · 2019
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Contextualized role interaction for neural machine translation, 2019
Dirk Weissenborn, Douwe Kiela, Jason Weston, and Kyunghyun Cho · 2019
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Pay less attention with lightweight and dynamic convolutions
Felix Wu, Angela Fan, Alexei Baevski, Yann Dauphin, and Michael Auli · 2019
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Tied transformers: Neural machine translation with shared encoder and decoder
Yingce Xia, Tianyu He, Xu Tan, Fei Tian, Di He, and Tao Qin · 2019
Later among the works it cites.