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This paper describes LIUM submissions to WMT17 News Translation Task for English-German, English-Turkish, English-Czech and English-Latvian language pairs.
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.
Frank Vanden Berghen and Hugues Bersini. 2005 · 2004
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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, Chris Dyer, Ondřej Bojar, Alexandra Constantin, and Evan Herbst. 2007 · 2007
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio. 2010 · 2010
Earlier work this paper cites.
Recurrent neural network based language model
Tomas Mikolov, Martin Karafiát, Lukás Burget, Jan Cernocký, and Sanjeev Khudanpur. 2010 · 2010
Earlier work this paper cites.
Continuous-space language models for statistical machine translation
Holger Schwenk. 2010 · 2010
Earlier work this paper cites.
Morphological analysis with limited resources: Latvian example
Pēteris Paikens, Laura Rituma, and Lauma Pretkalniņa. 2013 · 2013
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013 · 2013
Cited alongside, same era.
XenC: An open-source tool for data selection in natural language processing
Anthony Rousseau. 2013 · 2013
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, 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.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Conditional gated recurrent unit with attention mechanism
Orhan Firat and Kyunghyun Cho. 2016 · 2016
Later among the works it cites.
Factored neural machine translation architectures
Mercedes García-Martínez, Loïc Barrault, and Fethi Bougares. 2016 · 2016
Later among the works it cites.
Tying word vectors and word classifiers: A loss framework for language modeling
Hakan Inan, Khashayar Khosravi, and Richard Socher. 2016 · 2016
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Evaluating the morphological competence of machine translation systems
Franck Burlot and François Yvon. 2017 · 2017
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Nmtpy: A flexible toolkit for advanced neural machine translation systems
Ozan Caglayan, Mercedes García-Martínez, Adrien Bardet, Walid Aransa, Fethi Bougares, and Loïc Barrault. 2017 · 2017
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Open-source tools for morphology, lemmatization, pos tagging and named entity recognition
Jana Straková, Milan Straka, and Jan Hajič. 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.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
Cited alongside, same era.
Improving neural machine translation models with monolingual data
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.
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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Using the output embedding to improve language models
Ofir Press and Lior Wolf. 2017 · 2025
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