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This paper addresses the task of contextual translation using multi-segment models.
Microsoft translator at wmt 2019: Towards large-scale document-level neural machine translation
Marcin Junczys-Dowmunt. 2019a · 1907
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Evaluating pronominal anaphora in machine translation: An evaluation measure and a test suite
Prathyusha Jwalapuram, Shafiq Joty, Irina Temnikova, and Preslav Nakov. 2019 · 1909
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A survey on document-level machine translation: Methods and evaluation
Sameen Maruf, Fahimeh Saleh, and Gholamreza Haffari. 2019b · 1912
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Does multi-encoder help? A case study on context-aware neural machine translation
Bei Li, Hui Liu, Ziyang Wang, Yufan Jiang, Tong Xiao, Jingbo Zhu, Tongran Liu, and Changliang Li. 2020 · 2005
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Andrew Hayes and Klaus Krippendorff. 2007 · 2007
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Jingjing Huo, Christian Herold, Yingbo Gao, Leonard Dahlmann, Shahram Khadivi, and Hermann Ney. 2020 · 2010
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Pronoun-targeted fine-tuning for nmt with hybrid losses
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WIT3: Web inventory of transcribed and translated talks
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Distilling the knowledge in a neural network
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Sequence-level knowledge distillation
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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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Neural machine translation with extended context
Jörg Tiedemann and Yves Scherrer. 2017 · 2017
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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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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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Document-level neural machine translation with hierarchical attention networks
Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, and James Henderson. 2018 · 2018
The sockeye 2 neural machine translation toolkit at AMTA 2020
Tobias Domhan, Michael Denkowski, David Vilar, Xing Niu, Felix Hieber, and Kenneth Heafield. 2020 · 2020
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Document-level neural MT: A systematic comparison
António Lopes, M. Amin Farajian, Rachel Bawden, Michael Zhang, and André F. T. Martins. 2020 · 2020
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Understanding knowledge distillation in non-autoregressive machine translation
Chunting Zhou, Jiatao Gu, and Graham Neubig. 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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G-transformer for document-level machine translation
Guangsheng Bao, Yue Zhang, Zhiyang Teng, Boxing Chen, and Weihua Luo. 2021 · 2021
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Diverse pretrained context encodings improve document translation
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A Large-Scale Test Set for the Evaluation of Context-Aware Pronoun Translation in Neural Machine Translation
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Explaining sequence-level knowledge distillation as data-augmentation for neural machine translation
Mitchell A. Gordon and Kevin Duh. 2019 · 2019
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Microsoft translator at WMT 2019: Towards large-scale document-level neural machine translation
Marcin Junczys-Dowmunt. 2019b · 2019
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Selective attention for context-aware neural machine translation
Sameen Maruf, André F. T. Martins, and Gholamreza Haffari. 2019a · 2019
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Context-aware monolingual repair for neural machine translation
Elena Voita, Rico Sennrich, and Ivan Titov. 2019a · 2019
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Findings of the 2020 conference on machine translation (WMT20)
Loïc Barrault, Magdalena Biesialska, Ondřej Bojar, Marta R. Costa-jussà, Christian Federmann, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Matthias Huck, Eric Joanis, Tom Kocmi, Philipp Koehn, Chi-kiu Lo, Nikola Ljubešić, Christof Monz, Makoto Morishita, Masaaki Nagata, Toshiaki Nakazawa, Santanu Pal, Matt Post, and Marcos Zampieri. 2020 · 2020
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Distilling multiple domains for neural machine translation
Anna Currey, Prashant Mathur, and Georgiana Dinu. 2020 · 2020
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Domenic Donato, Lei Yu, and Chris Dyer. 2021 · 2021
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Measuring and increasing context usage in context-aware machine translation
Patrick Fernandes, Kayo Yin, Graham Neubig, and André F. T. Martins. 2021 · 2021
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On user interfaces for large-scale document-level human evaluation of machine translation outputs
Roman Grundkiewicz, Marcin Junczys-Dowmunt, Christian Federmann, and Tom Kocmi. 2021 · 2021
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Data augmentation by concatenation for low-resource translation: A mystery and a solution
Toan Q. Nguyen, Kenton Murray, and David Chiang. 2021 · 2021
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On the limits of minimal pairs in contrastive evaluation
Jannis Vamvas and Rico Sennrich. 2021 · 2021
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How does distilled data complexity impact the quality and confidence of non-autoregressive machine translation?
Weijia Xu, Shuming Ma, Dongdong Zhang, and Marine Carpuat. 2021 · 2021
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