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While most neural machine translation (NMT) systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can substantially improve final translation accuracy.
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.
Minimum error rate training in statistical machine translation
Franz Josef Och. 2003 · 2003
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Statistical significance tests for machine translation evaluation
Philipp Koehn. 2004 · 2004
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Orange: a method for evaluating automatic evaluation metrics for machine translation
Chin-Yew Lin and Franz Josef Och. 2004 · 2004
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Minimum risk annealing for training log-linear models
David A. Smith and Jason Eisner. 2006 · 2006
Earlier work this paper cites.
Online large-margin training for statistical machine translation
Taro Watanabe, Jun Suzuki, Hajime Tsukada, and Hideki Isozaki. 2007 · 2007
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The best lexical metric for phrase-based statistical mt system optimization
Daniel Cer, Christopher D. Manning, and Daniel Jurafsky. 2010 · 2010
Earlier work this paper cites.
Findings of the 2011 workshop on statistical machine translation
Chris Callison-Burch, Philipp Koehn, Christof Monz, and Omar Zaidan. 2011 · 2011
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Tuning as ranking
Mark Hopkins and Jonathan May. 2011 · 2011
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Better evaluation metrics lead to better machine translation
Chang Liu, Daniel Dahlmeier, and Hwee Tou Ng. 2011 · 2011
Earlier work this paper cites.
SemEval-2012 task 6: A pilot on semantic textual similarity
Eneko Agirre, Mona Diab, Daniel Cer, and Aitor Gonzalez-Agirre. 2012 · 2012
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2012 · 2012
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* sem 2013 shared task: Semantic textual similarity
Eneko Agirre, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, and Weiwei Guo. 2013 · 2013
Earlier work this paper cites.
Improving machine translation by training against an automatic semantic frame based evaluation metric
Chi-kiu Lo, Karteek Addanki, Markus Saers, and Dekai Wu. 2013 · 2013
Earlier work this paper cites.
On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. 2013 · 2013
Earlier work this paper cites.
SemEval-2014 task 10: Multilingual semantic textual similarity
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2014 · 2014
Cited alongside, same era.
Improving MEANT based semantically tuned SMT
Meriem Beloucif, Chi-kiu Lo, and Dekai Wu. 2014 · 2014
Cited alongside, same era.
Meteor universal: Language specific translation evaluation for any target language
Michael Denkowski and Alon Lavie. 2014 · 2014
Cited alongside, same era.
Distributed representations of sentences and documents
Quoc V. Le and Tomas Mikolov. 2014 · 2014
Cited alongside, same era.
SemEval-2015 task 2: Semantic textual similarity, English, Spanish and pilot on interpretability
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Inigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, and Janyce Wiebe. 2015 · 2015
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
Later among the works it cites.
Towards universal paraphrastic sentence embeddings
John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2016 · 2016
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Sequence-to-sequence learning as beam-search optimization
Sam Wiseman and Alexander M. Rush. 2016 · 2016
Later among the works it cites.
Findings of the 2017 conference on machine translation (wmt17)
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Shujian Huang, Matthias Huck, Philipp Koehn, Qun Liu, Varvara Logacheva, et al. 2017 · 2017
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
Jointly optimizing word representations for lexical and sentential tasks with the c-phrase model
Nghia The Pham, Germán Kruszewski, Angeliki Lazaridou, and Marco Baroni. 2015 · 2015
Cited alongside, same era.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015 · 2015
Cited alongside, same era.
Minimum risk training for neural machine translation
Shiqi Shen, Yong Cheng, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2015 · 2015
Cited alongside, same era.
Results of the wmt15 tuning shared task
Miloš Stanojević, Amir Kamran, and Ondřej Bojar. 2015 · 2015
Cited alongside, same era.
SemEval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation
Eneko Agirre, Carmen Banea, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2016 · 2016
Cited alongside, same era.
Learning distributed representations of sentences from unlabelled data
Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016 · 2016
Cited alongside, same era.
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017 · 2017
Later among the works it cites.
Unsupervised learning of sentence embeddings using compositional n-gram features
Matteo Pagliardini, Prakhar Gupta, and Martin Jaggi. 2017 · 2017
Later among the works it cites.
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton. 2017 · 2017
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Findings of the 2018 conference on machine translation (wmt18)
Ondřej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Philipp Koehn, and Christof Monz. 2018 · 2018
Later among the works it cites.
Classical structured prediction losses for sequence to sequence learning
Sergey Edunov, Myle Ott, Michael Auli, David Grangier, et al. 2018 · 2018
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ParaNMT-50M: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations
John Wieting and Kevin Gimpel. 2018 · 2018
Later among the works it cites.
compare-mt: A tool for holistic comparison of language generation systems
Graham Neubig, Zi-Yi Dou, Junjie Hu, Paul Michel, Danish Pruthi, Xinyi Wang, and John Wieting. 2019 · 2019
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Simple and effective paraphrastic similarity from parallel translations
John Wieting, Kevin Gimpel, Graham Neubig, and Taylor Berg-Kirkpatrick. 2019 · 2019
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Revisiting recurrent networks for paraphrastic sentence embeddings
John Wieting and Kevin Gimpel. 2017 · 2088
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