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A major impediment to the transition to context-aware machine translation is the absence of good evaluation metrics and test sets.
PROTEST: A test suite for evaluating pronouns in machine translation
Liane Guillou and Christian Hardmeier. 2016 · 2016
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OpenSubtitles2016: Extracting large parallel corpora from movie and TV subtitles
Pierre Lison and Jörg Tiedemann. 2016 · 2016
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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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Evaluating discourse phenomena in neural machine translation
Rachel Bawden, Rico Sennrich, Alexandra Birch, and Barry Haddow. 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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Attaining the unattainable? reassessing claims of human parity in neural machine translation
Antonio Toral, Sheila Castilho, Ke Hu, and Andy Way. 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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Microsoft translator at wmt 2019: Towards large-scale document-level neural machine translation
Marcin Junczys-Dowmunt. 2019 · 2019
Cited alongside, same era.
When a good translation is wrong in context: Context-aware machine translation improves on deixis, ellipsis, and lexical cohesion
Elena Voita, Rico Sennrich, and Ivan Titov. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
SimAlign: High quality word alignments without parallel training data using static and contextualized embeddings
Masoud Jalili Sabet, Philipp Dufter, François Yvon, and Hinrich Schütze. 2020 · 2020
Cited alongside, same era.
Document-level neural MT: A systematic comparison
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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Findings of the 2022 conference on machine translation (WMT22)
Tom Kocmi, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Thamme Gowda, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Rebecca Knowles, Philipp Koehn, Christof Monz, Makoto Morishita, Masaaki Nagata, Toshiaki Nakazawa, Michal Novák, Martin Popel, and Maja Popović. 2022 · 2022
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CoCoA-MT: A dataset and benchmark for contrastive controlled MT with application to formality
Maria Nadejde, Anna Currey, Benjamin Hsu, Xing Niu, Marcello Federico, and Georgiana Dinu. 2022 · 2022
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F-coref: Fast, accurate and easy to use coreference resolution
Shon Otmazgin, Arie Cattan, and Yoav Goldberg. 2022 · 2022
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Rethinking document-level neural machine translation
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António Lopes, M. Amin Farajian, Rachel Bawden, Michael Zhang, and André F. T. Martins. 2020 · 2020
Cited alongside, same era.
COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
G-transformer for document-level machine translation
Guangsheng Bao, Yue Zhang, Zhiyang Teng, Boxing Chen, and Weihua Luo. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Zewei Sun, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Shujian Huang, Jiajun Chen, and Lei Li. 2022 · 2022
Later among the works it cites.
Embarrassingly easy document-level MT metrics: How to convert any pretrained metric into a document-level metric
Giorgos Vernikos, Brian Thompson, Prashant Mathur, and Marcello Federico. 2022 · 2022
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Does sentence segmentation matter for machine translation?
Rachel Wicks and Matt Post. 2022 · 2022
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When does translation require context? a data-driven, multilingual exploration
Patrick Fernandes, Kayo Yin, Emmy Liu, André Martins, and Graham Neubig. 2023 · 2023
Closest in time.
Escaping the sentence-level paradigm in machine translation
Matt Post and Marcin Junczys-Dowmunt. 2023 · 2023
Closest in time.