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It is well-known that document context is vital for resolving a range of translation ambiguities, and in fact the document setting is the most natural setting for nearly all translation.
A survey on document-level machine translation: Methods and evaluation
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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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Jungo Kasai, Nikolaos Pappas, Hao Peng, James Cross, and Noah A. Smith. 2020 · 2006
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Context-aware discriminative phrase selection for statistical machine translation
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Evaluation of context-dependent phrasal translation lexicons for statistical machine translation
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Findings of the 2015 workshop on statistical machine translation
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Liane Guillou and Christian Hardmeier. 2016 · 2016
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Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Annette Rios Gonzales, Laura Mascarell, and Rico Sennrich. 2017 · 2017
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Tilde MODEL - multilingual open data for EU languages
Roberts Rozis and Raivis Skadiņš. 2017 · 2017
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Neural machine translation with extended context
Jörg Tiedemann and Yves Scherrer. 2017 · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Marian: Fast neural machine translation in C++
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Modeling coherence for neural machine translation with dynamic and topic caches
Shaohui Kuang, Deyi Xiong, Weihua Luo, and Guodong 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
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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
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A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
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Fast lexically constrained decoding with dynamic beam allocation for neural machine translation
Matt Post and David Vilar. 2018 · 2018
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Why the time is ripe for discourse in machine translation
Rico Sennrich. 2018 · 2018
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Learning to remember translation history with a continuous cache
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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COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020 · 2020
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Contextual neural machine translation improves translation of cataphoric pronouns
KayYen Wong, Sameen Maruf, and Gholamreza Haffari. 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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Findings of the 2021 conference on machine translation (WMT21)
Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ondřej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R. Costa-jussa, Cristina España-Bonet, Angela Fan, Christian Federmann, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Leonie Harter, Kenneth Heafield, Christopher Homan, Matthias Huck, Kwabena Amponsah-Kaakyire, Jungo Kasai, Daniel Khashabi, Kevin Knight, Tom Kocmi, Philipp Koehn, Nicholas Lourie, Christof Monz, Makoto Morishita, Masaaki Nagata, Ajay Nagesh, Toshiaki Nakazawa, Matteo Negri, Santanu Pal, Allahsera Auguste Tapo, Marco Turchi, Valentin Vydrin, and Marcos Zampieri. 2021 · 2021
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Zhaopeng Tu, Yang Liu, Shuming Shi, and Tong Zhang. 2018 · 2018
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Context-aware neural machine translation learns anaphora resolution
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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
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Findings of the 2019 conference on machine translation (WMT19)
Loïc Barrault, Ondřej Bojar, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias Müller, Santanu Pal, Matt Post, and Marcos Zampieri. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Star-transformer
Qipeng Guo, Xipeng Qiu, Pengfei Liu, Yunfan Shao, Xiangyang Xue, and Zheng Zhang. 2019 · 2019
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Microsoft translator at WMT 2019: Towards large-scale document-level neural machine translation
Marcin Junczys-Dowmunt. 2019 · 2019
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Discourse connectives across languages: Factors influencing their explicit or implicit translation
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BlonDe: An automatic evaluation metric for document-level machine translation
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Findings of the 2022 conference on machine translation (WMT22)
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Embarrassingly easy document-level MT metrics: How to convert any pretrained metric into a document-level metric
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How good are gpt models at machine translation? a comprehensive evaluation
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Encoding sentence position in context-aware neural machine translation with concatenation
Lorenzo Lupo, Marco Dinarelli, and Laurent Besacier. 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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Document flattening: Beyond concatenating context for document-level neural machine translation
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Prompting large language model for machine translation: A case study
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