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Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art Neural Machine Translation (NMT) with their capability in modeling complex functions and capturing complex linguistic structures.
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Neural machine translation by jointly learning to align and translate
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Christian Buck, Kenneth Heafield, and Bas Van Ooyen. 2014 · 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 · 2014
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Alex Graves, Greg Wayne, and Ivo Danihelka. 2014 · 2014
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Addressing the rare word problem in neural machine translation
Neural transformation machine: A new architecture for sequence-to-sequence learning
Fandong Meng, Zhengdong Lu, Zhaopeng Tu, Hang Li, and Qun Liu. 2015 · 2015
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Minimum risk training for neural machine translation
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Minh-Thang Luong, Ilya Sutskever, Quoc V Le, Oriol Vinyals, and Wojciech Zaremba. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015 · 2015
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On using very large target vocabulary for neural machine translation
Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. 2015 · 2015
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Nal Kalchbrenner, Ivo Danihelka, and Alex Graves. 2015 · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015 · 2015
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How to construct deep recurrent neural networks
Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2013a
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013b
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Modeling coverage for neural machine translation
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Google’s neural machine translation system: Bridging the gap between human and machine translation
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Deep recurrent models with fast-forward connections for neural machine translation
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