Fetching the paper…
Reading the bibliography…
An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences.
A statistical approach to machine translation
Peter F. Brown, John Cocke, Stephen Della Pietra, Vincent J. Della Pietra, Frederick Jelinek, John D. Lafferty, Robert L. Mercer, and Paul S. Roossin. 1990 · 1990
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 phrase-based translation
Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003 · 2003
Earlier work this paper cites.
Large language models in machine translation
Thorsten Brants, Ashok C. Popat, Peng Xu, Franz Josef Och, and Jeffrey Dean. 2007 · 2007
Earlier work this paper cites.
Moses: Open source toolkit for statistical machine translation
Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, and Evan Herbst. 2007 · 2007
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, , and Pierre-Antoine Manzagol. 2008 · 2008
Earlier work this paper cites.
Domain adaptation for statistical machine translation with monolingual resources
Nicola Bertoldi and Marcello Federico. 2009 · 2009
Earlier work this paper cites.
Statistical machine translation
Philipp Koehn. 2010 · 2010
Earlier work this paper cites.
Improving translation model by monolingual data
Ondrej Bojar and Ales Tamchyna. 2011 · 2011
Earlier work this paper cites.
Investigations on translation model adaptation using monolingual data
Patrik Lambert, Holger Schwenk, Christophe Servan, and Sadaf Abdul-Rauf. 2011 · 2011
Earlier work this paper cites.
langid. py: An off-the-shelf language identification tool
Marco Lui and Timothy Baldwin. 2012 · 2012
Earlier work this paper cites.
Generating sequences with recurrent neural networks
Alex Graves. 2013 · 2013
Earlier work this paper cites.
Scalable Modified Kneser-Ney Language Model Estimation
Kenneth Heafield, Ivan Pouzyrevsky, Jonathan H. Clark, and Philipp Koehn. 2013 · 2013
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
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
On using monolingual corpora in neural machine translation
Caglar Gulcehre, Orhan Firat, Kelvin Xu, Kyunghyun Cho, Loic Barrault, Huei-Chi Lin, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015 · 2015
Cited alongside, same era.
Rethinking the Inception Architecture for Computer Vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. 2015 · 2015
Cited alongside, same era.
Semi-supervised learning for neural machine translation
Yong Cheng, Wei Xu, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016 · 2016
Cited alongside, same era.
Toward multilingual neural machine translation with universal encoder and decoder
Thanh-Le Ha, Jan Niehues, and Alexander H. Waibel. 2016 · 2016
Cited alongside, same era.
Dreaming more data: Class-dependent distributions over diffeomorphisms for learned data augmentation
Soren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John W. Fisher, and Lars Kai Hansen. 2016 · 2016
Cited alongside, same era.
Neural machine translation for low-resource languages without parallel corpora
Alina Karakanta, Jon Dehdari, and Josef van Genabith. 2017 · 2017
Later among the works it cites.
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton. 2017 · 2017
Later among the works it cites.
The effectiveness of data augmentation in image classification using deep learning
Luis Perez and Jason Wang. 2017 · 2017
Later among the works it cites.
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
Later among the works it cites.
Dual supervised learning
Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, and Tie-Yan Liu. 2017 · 2017
Later among the works it cites.
Findings of the 2018 conference on machine translation (WMT18)
Ondřej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, and Christof Monz. 2018 · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning distributed representations of sentences from unlabelled data
Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016 · 2016
Cited alongside, same era.
Neural machine translation in linear time
Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aäron van den Oord, Alex Graves, and Koray Kavukcuoglu. 2016 · 2016
Cited alongside, same era.
Building end-to-end dialogue systems using generative hierarchical neural network models
Iulian Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C. Courville, and Joelle Pineau. 2016 · 2016
Cited alongside, same era.
Exploiting source-side monolingual data in neural machine translation
Jiajun Zhang and Chengqing Zong. 2016 · 2016
Cited alongside, same era.
Weighted transformer network for machine translation
Karim Ahmed, Nitish Shirish Keskar, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Data augmentation generative adversarial networks
Antreas Antoniou, Amos J. Storkey, and Harrison Edwards. 2017 · 2017
Cited alongside, same era.
Copied Monolingual Data Improves Low-Resource Neural Machine Translation
Anna Currey, Antonio Valerio Miceli Barone, and Kenneth Heafield. 2017 · 2017
Cited alongside, same era.
Closest in time.
Explaining and generalizing back-translation through wake-sleep
Ryan Cotterell and Julia Kreutzer. 2018 · 2018
Closest in time.
Hierarchical neural story generation
Angela Fan, Yann Dauphin, and Mike Lewis. 2018 · 2018
Closest in time.
Universal neural machine translation for extremely low resource languages
Jiatao Gu, Hany Hassan, Jacob Devlin, and Victor O. K. Li. 2018 · 2018
Closest in time.
Achieving human parity on automatic chinese to english news translation
Hany Hassan, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, William Lewis, Mu Li, et al. 2018 · 2018
Closest in time.
Iterative back-translation for neural machine translation
Vu Cong Duy Hoang, Philipp Koehn, Gholamreza Haffari, and Trevor Cohn. 2018 · 2018
Closest in time.
Enhancement of encoder and attention using target monolingual corpora in neural machine translation
Kenji Imamura, Atsushi Fujita, and Eiichiro Sumita. 2018 · 2018
Closest in time.
Bi-directional neural machine translation with synthetic parallel data
Xing Niu, Michael Denkowski, and Marine Carpuat. 2018 · 2018
Closest in time.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
Closest in time.
Investigating backtranslation in neural machine translation
Alberto Poncelas, Dimitar Sht. Shterionov, Andy Way, Gideon Maillette de Buy Wenniger, and Peyman Passban. 2018 · 2018
Closest in time.
A call for clarity in reporting bleu scores
Matt Post. 2018 · 2018
Closest in time.
Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. 2018 · 2018
Closest in time.