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Despite the known limitations, most machine translation systems today still operate on the sentence-level.
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 · 1904
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Fill in the blanks: Imputing missing sentences for larger-context neural machine translation
Sébastien Jean, Ankur Bapna, and Orhan Firat. 2019 · 1910
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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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Europarl: A parallel corpus for statistical machine translation
Philipp Koehn. 2005 · 2005
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Amane Sugiyama and Naoki Yoshinaga. 2020 · 2010
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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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On using monolingual corpora in neural machine translation
Çaglar Gülçehre, Orhan Firat, Kelvin Xu, Kyunghyun Cho, Loïc Barrault, Huei-Chi Lin, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
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Overview of the IWSLT 2017 evaluation campaign
Mauro Cettolo, Marcello Federico, Luisa Bentivogli, Jan Niehues, Sebastian Stüker, Katsuhito Sudoh, Koichiro Yoshino, and Christian Federmann. 2017 · 2017
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Does neural machine translation benefit from larger context?
Sébastien Jean, Stanislas Lauly, Orhan Firat, and Kyunghyun Cho. 2017 · 2017
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Neural machine translation with extended context
Jörg Tiedemann and Yves Scherrer. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Evaluating discourse phenomena in neural machine translation
Rachel Bawden, Rico Sennrich, Alexandra Birch, and Barry Haddow. 2018 · 2018
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Achieving human parity on automatic chinese to english news translation
Hany Hassan, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt, William Lewis, Mu Li, Shujie Liu, Tie-Yan Liu, Renqian Luo, Arul Menezes, Tao Qin, Frank Seide, Xu Tan, Fei Tian, Lijun Wu, Shuangzhi Wu, Yingce Xia, Dongdong Zhang, Zhirui Zhang, and Ming Zhou. 2018 · 2018
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Fusing recency into neural machine translation with an inter-sentence gate model
Shaohui Kuang and Deyi Xiong. 2018 · 2018
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Subword regularization: Improving neural network translation models with multiple subword candidates
Taku Kudo. 2018 · 2018
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Has machine translation achieved human parity? A case for document-level evaluation
Samuel Läubli, Rico Sennrich, and Martin Volk. 2018 · 2018
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Opensubtitles2018: Statistical rescoring of sentence alignments in large, noisy parallel corpora
Pierre Lison, Jörg Tiedemann, and Milen Kouylekov. 2018 · 2018
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Document context neural machine translation with memory networks
Sameen Maruf and Gholamreza Haffari. 2018 · 2018
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Document-level neural machine translation with hierarchical attention networks
Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, and James Henderson. 2018 · 2018
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A large-scale test set for the evaluation of context-aware pronoun translation in neural machine translation
Mathias Müller, Annette Rios, Elena Voita, and Rico Sennrich. 2018 · 2018
Cited alongside, same era.
A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
Cited alongside, same era.
Context-aware neural machine translation learns anaphora resolution
Elena Voita, Pavel Serdyukov, Rico Sennrich, and Ivan Titov. 2018 · 2018
Cited alongside, same era.
Improving the transformer translation model with document-level context
Jiacheng Zhang, Huanbo Luan, Maosong Sun, Feifei Zhai, Jingfang Xu, Min Zhang, and Yang Liu. 2018 · 2018
Cited alongside, same era.
Naver labs europe’s systems for the WMT19 machine translation robustness task
Alexandre Berard, Ioan Calapodescu, and Claude Roux. 2019 · 2019
Cited alongside, same era.
Paracrawl: Web-scale parallel corpora for the languages of the EU
COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C. Farinha, and Alon Lavie. 2020 · 2020
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Better document-level machine translation with bayes’ rule
Lei Yu, Laurent Sartran, Wojciech Stokowiec, Wang Ling, Lingpeng Kong, Phil Blunsom, and Chris Dyer. 2020 · 2020
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Incorporating BERT into neural machine translation
Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu. 2020 · 2020
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Breaking the corpus bottleneck for context-aware neural machine translation with cross-task pre-training
Linqing Chen, Junhui Li, Zhengxian Gong, Boxing Chen, Weihua Luo, Min Zhang, and Guodong Zhou. 2021a · 2021
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Improving context-aware neural machine translation with source-side monolingual documents
Linqing Chen, Junhui Li, Zhengxian Gong, Xiangyu Duan, Boxing Chen, Weihua Luo, Min Zhang, and Guodong Zhou. 2021b · 2021
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Miquel Esplà-Gomis, Mikel L. Forcada, Gema Ramírez-Sánchez, and Hieu Hoang. 2019 · 2019
Cited alongside, same era.
Context-aware neural machine translation decoding
Eva Martínez Garcia, Carles Creus, and Cristina España-Bonet. 2019 · 2019
Cited alongside, same era.
Microsoft translator at WMT 2019: Towards large-scale document-level neural machine translation
Marcin Junczys-Dowmunt. 2019 · 2019
Cited alongside, same era.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E. Hinton. 2019 · 2019
Cited alongside, same era.
Naver labs europe’s systems for the document-level generation and translation task at WNGT 2019
Fahimeh Saleh, Alexandre Berard, Ioan Calapodescu, and Laurent Besacier. 2019 · 2019
Cited alongside, same era.
Cued@wmt19: Ewc&lms
Felix Stahlberg, Danielle Saunders, Adrià de Gispert, and Bill Byrne. 2019 · 2019
Cited alongside, same era.
Data augmentation using back-translation for context-aware neural machine translation
Amane Sugiyama and Naoki Yoshinaga. 2019 · 2019
Cited alongside, same era.
Ccmatrix: Mining billions of high-quality parallel sentences on the web
Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave, Armand Joulin, and Angela Fan. 2021 · 2021
Later among the works it cites.
Context-aware decoder for neural machine translation using a target-side document-level language model
Amane Sugiyama and Naoki Yoshinaga. 2021 · 2021
Later among the works it cites.
Mt-geneval: A counterfactual and contextual dataset for evaluating gender accuracy in machine translation
Anna Currey, Maria Nadejde, Raghavendra Reddy Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, and Georgiana Dinu. 2022 · 2022
Later among the works it cites.
Blonde: An automatic evaluation metric for document-level machine translation
Yuchen Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, and Ming Zhou. 2022 · 2022
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A survey on document-level neural machine translation: Methods and evaluation
Sameen Maruf, Fahimeh Saleh, and Gholamreza Haffari. 2022 · 2022
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Rethinking document-level neural machine translation
Zewei Sun, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Shujian Huang, Jiajun Chen, and Lei Li. 2022 · 2022
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Exploring paracrawl for document-level neural machine translation
Yusser Al Ghussin, Jingyi Zhang, and Josef van Genabith. 2023 · 2023
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How good are GPT models at machine translation? A comprehensive evaluation
Amr Hendy, Mohamed Abdelrehim, Amr Sharaf, Vikas Raunak, Mohamed Gabr, Hitokazu Matsushita, Young Jin Kim, Mohamed Afify, and Hany Hassan Awadalla. 2023 · 2023
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Improving language model integration for neural machine translation
Christian Herold, Yingbo Gao, Mohammad Zeineldeen, and Hermann Ney. 2023 · 2023
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Improving long context document-level machine translation
Christian Herold and Hermann Ney. 2023a · 2023
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On search strategies for document-level neural machine translation
Christian Herold and Hermann Ney. 2023b · 2023
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Marzena Karpinska and Mohit Iyyer. 2023 · 2023
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Escaping the sentence-level paradigm in machine translation
Matt Post and Marcin Junczys-Dowmunt. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Document-level machine translation with large language models
Longyue Wang, Chenyang Lyu, Tianbo Ji, Zhirui Zhang, Dian Yu, Shuming Shi, and Zhaopeng Tu. 2023 · 2023
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Prompting large language model for machine translation: A case study
Biao Zhang, Barry Haddow, and Alexandra Birch. 2023 · 2023
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