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Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train.
Fine-tune BERT for extractive summarization
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Bleu: a method for automatic evaluation of machine translation
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Rouge: a package for automatic evaluation of summaries
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Moses: Open source toolkit for statistical machine translation
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
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A clockwork rnn
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Sequence to sequence learning with neural networks
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
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A deep memory-based architecture for sequence-to-sequence learning
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 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, and Klaus Macherey. 2016 · 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 · 2016
Layer-wise coordination between encoder and decoder for neural machine translation
Tianyu He, Xu Tan, Yingce Xia, Di He, Tao Qin, Zhibo Chen, and Tie-Yan Liu. 2018 · 2018
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Scaling neural machine translation
Myle Ott, Sergey Edunov, David Grangier, and Michael Auli. 2018 · 2018
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Dense information flow for neural machine translation
Yanyao Shen, Xu Tan, Di He, Tao Qin, and Tie-Yan Liu. 2018 · 2018
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Hierarchical multiscale recurrent neural networks
Junyoung Chung, Sungjin Ahn, and Yoshua Bengio. 2016 · 2017
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Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017 · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Deep neural machine translation with linear associative unit
Mingxuan Wang, Zhengdong Lu, Jie Zhou, and Qun Liu. 2017 · 2017
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Training deeper neural machine translation models with transparent attention
Ankur Bapna, Mia Chen, Orhan Firat, Yuan Cao, and Yonghui Wu. 2018 · 2018
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Deep layer aggregation
Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell. 2018 · 2018
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Character-level language modeling with deeper self-attention
Rami Al-Rfou, Dokook Choe, Noah Constant, Mandy Guo, and Llion Jones. 2019 · 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 · 2019
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Dynamic layer aggregation for neural machine translation with routing-by-agreement
Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Longyue Wang, Shuming Shi, and Tong Zhang. 2019 · 2019
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Pre-trained language model representations for language generation
Sergey Edunov, Alexei Baevski, and Michael Auli. 2019 · 2019
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Depth growing for neural machine translation
Lijun Wu, Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, Jianhuang Lai, and Tie-Yan Liu. 2019 · 2019
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Improving deep transformer with depth-scaled initialization and merged attention
Biao Zhang, Ivan Titov, and Rico Sennrich. 2019a · 2019
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