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Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information.
The syntactic process , volume 24
Mark Steedman. 2000 · 2000
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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.
What’s in a translation rule?
Michel Galley, Mark Hopkins, Kevin Knight, and Daniel Marcu. 2004 · 2004
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On some pitfalls in automatic evaluation and significance testing for mt
Stefan Riezler and John T. Maxwell. 2005 · 2005
Earlier work this paper cites.
Ccg supertags in factored statistical machine translation
Alexandra Birch, Miles Osborne, and Philipp Koehn. 2007 · 2007
Earlier work this paper cites.
Hierarchical phrase-based translation
David Chiang. 2007 · 2007
Earlier work this paper cites.
Using dependency order templates to improve generality in translation
Arul Menezes and Chris Quirk. 2007 · 2007
Earlier work this paper cites.
Ghkm rule extraction and scope-3 parsing in moses
Philip Williams and Philipp Koehn. 2012 · 2012
Earlier work this paper cites.
Recurrent continuous translation models
Nal Kalchbrenner and Phil Blunsom. 2013 · 2013
Earlier work this paper cites.
Edinburgh’s Syntax-Based Machine Translation Systems
Maria Nadejde, Philip Williams, and Philipp Koehn. 2013 · 2013
Earlier work this paper cites.
Exploiting Synergies Between Open Resources for German Dependency Parsing, POS-tagging, and Morphological Analysis
Rico Sennrich, Martin Volk, and Gerold Schneider. 2013 · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Çağlar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014b · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 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.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
Joint a* ccg parsing and semantic role labelling
Mike Lewis, Luheng He, and Luke Zettlemoyer. 2015 · 2015
Cited alongside, same era.
Tree-to-sequence attentional neural machine translation
Akiko Eriguchi, Kazuma Hashimoto, and Yoshimasa Tsuruoka. 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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Multi-task sequence to sequence learning
Minh-Thang Luong, Quoc V Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. 2016 · 2016
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Factored Neural Machine Translation Architectures
Mercedes García Martínez, Loïc Barrault, and Fethi Bougares. 2016 · 2016
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Using factored word representation in neural network language models
Jan Niehues, Thanh-Le Ha, Eunah Cho, and Alex Waibel. 2016 · 2016
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Linguistic input features improve neural machine translation
Rico Sennrich and Barry Haddow. 2016 · 2016
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Rico Sennrich. 2015 · 2015
Cited alongside, same era.
Hindi CCGbank: CCG Treebank from the Hindi Dependency Treebank
Bharat Ram Ambati, Tejaswini Deoskar, and Mark Steedman. 2016 · 2016
Cited alongside, same era.
Globally normalized transition-based neural networks
Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins. 2016 · 2016
Cited alongside, same era.
Neural versus phrase-based machine translation quality: a case study
Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, and Marcello Federico. 2016 · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Recurrent neural network grammars
Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, and Noah A. Smith. 2016 · 2016
Cited alongside, same era.
On the properties of neural machine translation: Encoder–decoder approaches
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014a
Cited in the paper.
Later among the works it cites.
Does string-based neural mt learn source syntax?
Xing Shi, Inkit Padhi, and Kevin Knight. 2016 · 2016
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Towards string-to-tree neural machine translation
Roee Aharoni and Yoav Goldberg. 2017 · 2017
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Learning to parse and translate improves neural machine translation
Akiko Eriguchi, Yoshimasa Tsuruoka, and Kyunghyun Cho. 2017 · 2017
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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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