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We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search.
Hierarchical phrase-based translation
David Chiang. 2007 · 2003
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What’s in a translation rule?
Michel Galley, Mark Hopkins, Kevin Knight, and Daniel Marcu. 2004 · 2004
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Scalable inference and training of context-rich syntactic translation models
Michel Galley, Jonathan Graehl, Kevin Knight, Daniel Marcu, Steve DeNeefe, Wei Wang, and Ignacio Thayer. 2006 · 2006
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Learning performance of a machine translation system: a statistical and computational analysis
Marco Turchi, Tijl De Bie, and Nello Cristianini. 2008 · 2008
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Interpolated backoff for factored translation models
Philipp Koehn and Barry Haddow. 2012 · 2012
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Parallel data, tools and interfaces in opus
Jörg Tiedemann. 2012 · 2012
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A simple, fast, and effective reparameterization of ibm model 2
Chris Dyer, Victor Chahuneau, and Noah A. Smith. 2013 · 2013
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Combining bilingual and comparable corpora for low resource machine translation
Ann Irvine and Chris Callison-Burch. 2013 · 2013
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Recurrent continuous translation models
Nal Kalchbrenner and Phil Blunsom. 2013 · 2013
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On the properties of neural machine translation: Encoder–decoder approaches
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
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Overcoming the curse of sentence length for neural machine translation using automatic segmentation
Jean Pouget-Abadie, Dzmitry Bahdanau, Bart van Merrienboer, KyungHyun Cho, and Yoshua Bengio. 2014 · 2014
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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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Stanford neural machine translation systems for spoken language domains
Minh-Thang Luong and Christopher D Manning. 2015 · 2015
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Addressing the rare word problem in neural machine translation
Thang Luong, Ilya Sutskever, Quoc Le, Oriol Vinyals, and Wojciech Zaremba. 2015 · 2015
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Incorporating discrete translation lexicons into neural machine translation
Philip Arthur, Graham Neubig, and Satoshi Nakamura. 2016 · 2016
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The JHU machine translation systems for WMT 2016
Shuoyang Ding, Kevin Duh, Huda Khayrallah, Philipp Koehn, and Matt Post. 2016b · 2016
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Fast domain adaptation for neural machine translation
Markus Freitag and Yaser Al-Onaizan. 2016 · 2016
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Is neural machine translation ready for deployment? a case study on 30 translation directions
Marcin Junczys-Dowmunt, Tomasz Dwojak, and Hieu Hoang. 2016 · 2016
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Neural machine translation with supervised attention
Lemao Liu, Masao Utiyama, Andrew Finch, and Eiichiro Sumita. 2016 · 2016
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Modeling coverage for neural machine translation
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016 · 2016
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Edinburgh’s statistical machine translation systems for wmt16
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Neural versus phrase-based machine translation quality: a case study
Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, and Marcello Federico. 2016 · 2016
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Findings of the 2016 conference on machine translation
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurelie Neveol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, and Marcos Zampieri. 2016 · 2016
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Guided alignment training for topic-aware neural machine translation
Wenhu Chen, Evgeny Matusov, Shahram Khadivi, and Jan-Thorsten Peter. 2016 · 2016
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Systran’s pure neural machine translation systems
Josep Maria Crego, Jungi Kim, Guillaume Klein, Anabel Rebollo, Kathy Yang, Jean Senellart, Egor Akhanov, Patrice Brunelle, Aurelien Coquard, Yongchao Deng, Satoshi Enoue, Chiyo Geiss, Joshua Johanson, Ardas Khalsa, Raoum Khiari, Byeongil Ko, Catherine Kobus, Jean Lorieux, Leidiana Martins, Dang-Chuan Nguyen, Alexandra Priori, Thomas Riccardi, Natalia Segal, Christophe Servan, Cyril Tiquet, Bo Wang, Jin Yang, Dakun Zhang, Jing Zhou, and Peter Zoldan. 2016 · 2016
Cited alongside, same era.
The jhu machine translation systems for wmt 2016
Shuoyang Ding, Kevin Duh, Huda Khayrallah, Philipp Koehn, and Matt Post. 2016a · 2016
Cited alongside, same era.
Edinburgh neural machine translation systems for WMT 16
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016a
Cited in the paper.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016b
Cited in the paper.
Philip Williams, Rico Sennrich, Maria Nadejde, Matthias Huck, Barry Haddow, and Ondřej Bojar. 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, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
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Nematus: a toolkit for neural machine translation
Rico Sennrich, Orhan Firat, Kyunghyun Cho, Alexandra Birch, Barry Haddow, Julian Hitschler, Marcin Junczys-Dowmunt, Samuel Läubli, Antonio Valerio Miceli Barone, Jozef Mokry, and Maria Nadejde. 2017 · 2017
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A multifaceted evaluation of neural versus phrase-based machine translation for 9 language directions
Antonio Toral and Víctor M. Sánchez-Cartagena. 2017 · 2017
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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, Christopher J. Dyer, Ondřej Bojar, Alexandra Constantin, and Evan Herbst. 2007 · 2045
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