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Back-translation has proven to be an effective method to utilize monolingual data in neural machine translation (NMT), and iteratively conducting back-translation can further improve the model performance.
BLEU: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Robert C. Moore and William Lewis. 2010 · 2010
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Machine translation evaluation versus quality estimation
Lucia Specia, Dhwaj Raj, and Marco Turchi. 2010 · 2010
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Domain adaptation via pseudo in-domain data selection
Amittai Axelrod, Xiaodong He, and Jianfeng Gao. 2011 · 2011
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Kenneth Heafield. 2011 · 2011
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Parallel data, tools and interfaces in opus
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Adaptation data selection using neural language models: Experiments in machine translation
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Submodularity for data selection in machine translation
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Marco Turchi, Antonios Anastasopoulos, José GC de Souza, and Matteo Negri. 2014 · 2014
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SHEF-NN: Translation quality estimation with neural networks
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Multi-level translation quality prediction with QuEst++
Lucia Specia, Gustavo Paetzold, and Carolina Scarton. 2015 · 2015
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Yong Cheng, Wei Xu, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016 · 2016
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Dual learning for machine translation
Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma. 2016 · 2016
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A recurrent neural networks approach for estimating the quality of machine translation output
Hyun Kim and Jong-Hyeok Lee. 2016 · 2016
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Exploiting source-side monolingual data in neural machine translation
Jiajun Zhang and Chengqing Zong. 2016 · 2016
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Improving low-resource neural machine translation with filtered pseudo-parallel corpus
Aizhan Imankulova, Takayuki Sato, and Mamoru Komachi. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Dynamic data selection for neural machine translation
Marlies van der Wees, Arianna Bisazza, and Christof Monz. 2017 · 2017
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Explaining and generalizing back-translation through wake-sleep
Nju submissions for the wmt19 quality estimation shared task
Qi Hou, Shujian Huang, Tianhao Ning, Xinyu Dai, and Jiajun Chen. 2019 · 2019
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Domain adaptation of neural machine translation by lexicon induction
Junjie Hu, Mengzhou Xia, Graham Neubig, and Jaime Carbonell. 2019 · 2019
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Unbabel’s participation in the wmt19 translation quality estimation shared task
Fabio Kepler, Jonay Trénous, Marcos Treviso, Miguel Vera, António Góis, M Amin Farajian, António V Lopes, and André FT Martins. 2019 · 2019
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compare-mt: A tool for holistic comparison of language generation systems
Graham Neubig, Zi-Yi Dou, Junjie Hu, Paul Michel, Danish Pruthi, and Xinyi Wang. 2019 · 2019
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How multilingual is multilingual bert?
Telmo Pires, Eva Schlinger, and Dan Garrette. 2019 · 2019
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Ryan Cotterell and Julia Kreutzer. 2018 · 2018
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Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
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Iterative back-translation for neural machine translation
Vu Cong Duy Hoang, Philipp Koehn, Gholamreza Haffari, and Trevor Cohn. 2018 · 2018
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Dual conditional cross-entropy filtering of noisy parallel corpora
Marcin Junczys-Dowmunt. 2018 · 2018
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Bi-directional neural machine translation with synthetic parallel data
Xing Niu, Michael Denkowski, and Marine Carpuat. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Findings of the wmt 2019 shared tasks on quality estimation
Erick Fonseca, Lisa Yankovskaya, André FT Martins, Mark Fishel, and Christian Federmann. 2019 · 2019
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Competence-based curriculum learning for neural machine translation
Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, and Tom Mitchell. 2019 · 2019
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Data selection with cluster-based language difference models and cynical selection
Lucía Santamaría and Amittai Axelrod. 2019 · 2019
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Generalized data augmentation for low-resource translation
Mengzhou Xia, Xiang Kong, Antonios Anastasopoulos, and Graham Neubig. 2019 · 2019
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Curriculum learning for domain adaptation in neural machine translation
Xuan Zhang, Pamela Shapiro, Gaurav Kumar, Paul McNamee, Marine Carpuat, and Kevin Duh. 2019 · 2019
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Source: Source-conditional elmo-style model for machine translation quality estimation
Junpei Zhou, Zhisong Zhang, and Zecong Hu. 2019 · 2019
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Balancing training for multilingual neural machine translation
Xinyi Wang, Yulia Tsvetkov, and Graham Neubig. 2020 · 2020
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Mirror-generative neural machine translation
Zaixiang Zheng, Hao Zhou, Shujian Huang, Lei Li, Xin-Yu Dai, and Jia jun Chen. 2020 · 2020
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