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Machine translation (MT) systems translate text between different languages by automatically learning in-depth knowledge of bilingual lexicons, grammar and semantics from the training examples.
On the evaluation of machine translation systems trained with back-translation
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A statistical approach to machine translation
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Example-based machine translation
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The mathematics of statistical machine translation: Parameter estimation
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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
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Statistical phrase-based translation
Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003 · 2003
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The alignment template approach to statistical machine translation
Franz Josef Och and Hermann Ney. 2004 · 2004
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A unigram orientation model for statistical machine translation
Christoph Tillmann. 2004 · 2004
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A hierarchical phrase-based model for statistical machine translation
David Chiang. 2005 · 2005
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Tree-to-string alignment template for statistical machine translation
Yang Liu, Qun Liu, and Shouxun Lin. 2006 · 2006
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Improving translation quality by discarding most of the phrasetable
Howard Johnson, Joel Martin, George Foster, and Roland Kuhn. 2007 · 2007
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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, et al. 2007 · 2007
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Curriculum learning
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Statistical machine translation
Philipp Koehn. 2009 · 2009
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Accurate non-hierarchical phrase-based translation
Michel Galley and Christopher D. Manning. 2010 · 2010
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Automatic analysis of syntactic complexity in second language writing
Xiaofei Lu. 2010 · 2010
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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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Edinburgh’s phrase-based machine translation systems for wmt-14
Nadir Durrani, Barry Haddow, Philipp Koehn, and Kenneth Heafield. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 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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Neural reranking improves subjective quality of machine translation: Naist at wat2015
Graham Neubig, Makoto Morishita, and Satoshi Nakamura. 2015 · 2015
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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Neural machine translation advised by statistical machine translation
Xing Wang, Zhengdong Lu, Zhaopeng Tu, Hang Li, Deyi Xiong, and Min Zhang. 2017 · 2017
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Prior knowledge integration for neural machine translation using posterior regularization
Jiacheng Zhang, Yang Liu, Huanbo Luan, Jingfang Xu, and Maosong Sun. 2017 · 2017
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Phrase-based & neural unsupervised machine translation
Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018 · 2018
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Multi-head attention with disagreement regularization
Jian Li, Zhaopeng Tu, Baosong Yang, Michael R. Lyu, and Tong Zhang. 2018 · 2018
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Incorporating statistical machine translation word knowledge into neural machine translation
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Does string-based neural MT learn source syntax?
Xing Shi, Inkit Padhi, and Kevin Knight. 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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Exploiting source-side monolingual data in neural machine translation
Jiajun Zhang and Chengqing Zong. 2016 · 2016
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A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis and Tommi Jaakkola. 2017 · 2017
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What do neural machine translation models learn about morphology?
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass. 2017 · 2017
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Xing Wang, Zhaopeng Tu, and Min Zhang. 2018 · 2018
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Phrase table as recommendation memory for neural machine translation
Yang Zhao, Yining Wang, Jiajun Zhang, and Chengqing Zong. 2018 · 2018
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Non-autoregressive neural machine translation with enhanced decoder input
Junliang Guo, Xu Tan, Di He, Tao Qin, Linli Xu, and Tie-Yan Liu. 2019 · 2019
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Towards understanding neural machine translation with word importance
Shilin He, Zhaopeng Tu, Xing Wang, Longyue Wang, Michael Lyu, and Shuming Shi. 2019 · 2019
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Attention is not explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
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Fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 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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On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville. 2019 · 2019
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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon. 2019 · 2019
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter. 2019 · 2019
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Convolutional self-attention networks
Baosong Yang, Longyue Wang, Derek F. Wong, Lidia S. Chao, and Zhaopeng Tu. 2019 · 2019
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Revisiting self-training for neural sequence generation
Junxian He, Jiatao Gu, Jiajun Shen, and Marc’Aurelio Ranzato. 2020 · 2020
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