Fetching the paper…
Reading the bibliography…
In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence.These vectors are generated by parameters which are updated by back-propagation of translation errors through time.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Bleu: A method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Statistical machine translation through global lexical selection and sentence reconstruction
Srinivas Bangalore, Patrick Haffner, and Stephan Kanthak. 2007 · 2007
Earlier work this paper cites.
Extending statistical machine translation with discriminative and trigger-based lexicon models
Arne Mauser, Saša Hasan, and Hermann Ney. 2009 · 2009
Earlier work this paper cites.
A discriminative lexicon model for complex morphology
Minwoo Jeong, Kristina Toutanova, Hisami Suzuki, and Chris Quirk. 2010 · 2010
Earlier work this paper cites.
ADADELTA: an adaptive learning rate method
Matthew D. Zeiler. 2012 · 2012
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Çaglar Gülçehre, KyungHyun Cho, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
Cited alongside, same era.
Word translation prediction for morphologically rich languages with bilingual neural networks
Ke Tran, Arianna Bisazza, and Christof Monz. 2014 · 2014
Cited alongside, same era.
Multi-task learning for multiple language translation
Daxiang Dong, Hua Wu, Wei He, Dianhai Yu, and Haifeng Wang. 2015 · 2015
Cited alongside, same era.
Vocabulary selection strategies for neural machine translation
Gurvan L’Hostis, David Grangier, and Michael Auli. 2016 · 2016
Later among the works it cites.
Achieving open vocabulary neural machine translation with hybrid word-character models
Minh-Thang Luong and Christopher D. Manning. 2016 · 2016
Later among the works it cites.
Interactive attention for neural machine translation
Fandong Meng, Zhengdong Lu, Hang Li, and Qun Liu. 2016 · 2016
Later among the works it cites.
Vocabulary manipulation for neural machine translation
Haitao Mi, Zhiguo Wang, and Abe Ittycheriah. 2016 · 2016
Later among the works it cites.
Exploiting source-side monolingual data in neural machine translation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
On using very large target vocabulary for neural machine translation
Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015b · 2015
Cited alongside, same era.
Multi-task sequence to sequence learning
Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. 2015a
Cited in the paper.
Jiajun Zhang and Chengqing Zong. 2016 · 2016
Later among the works it cites.
Massive Exploration of Neural Machine Translation Architectures
Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc Le. 2017 · 2017
Closest in time.
Rnn-based encoder-decoder approach with word frequency estimation
Jun Suzuki and Masaaki Nagata. 2017 · 2017
Closest in time.