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We describe GEMBA, a GPT-based metric for assessment of translation quality, which works both with a reference translation and without.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020a · 1901
Earlier work this paper cites.
Findings of the 2011 workshop on statistical machine translation
Chris Callison-Burch, Philipp Koehn, Christof Monz, and Omar Zaidan. 2011 · 2011
Earlier work this paper cites.
Continuous measurement scales in human evaluation of machine translation
Yvette Graham, Timothy Baldwin, Alistair Moffat, and Justin Zobel. 2013 · 2013
Earlier work this paper cites.
Results of the WMT14 metrics shared task
Matouš Macháček and Ondřej Bojar. 2014 · 2014
Earlier work this paper cites.
Appraise evaluation framework for machine translation
Christian Federmann. 2018 · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
Results of the WMT20 metrics shared task
Nitika Mathur, Johnny Wei, Markus Freitag, Qingsong Ma, and Ondřej Bojar. 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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BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
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To ship or not to ship: An extensive evaluation of automatic metrics for machine translation
Tom Kocmi, Christian Federmann, Roman Grundkiewicz, Marcin Junczys-Dowmunt, Hitokazu Matsushita, and Arul Menezes. 2021 · 2021
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Findings of the 2022 conference on machine translation (wmt22)
Tom Kocmi, Rachel Bawden, Ondrej 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 Novak, Martin Popel, Maja Popovic, and Mariya Shmatova. 2022 · 2022
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Comet-22: Unbabel-ist 2022 submission for the metrics shared task
Ricardo Rei, Jose G. C. de Souza, Duarte Alves, Chrysoula Zerva, Ana C Farinha, Taisiya Glushkova, Alon Lavie, Luisa Coheur, and Andre F. T. Martins. 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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Prompting palm for translation: Assessing strategies and performance
David Vilar, Markus Freitag, Colin Cherry, Jiaming Luo, Viresh Ratnakar, and George Foster. 2022 · 2022
Later among the works it cites.
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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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
Cited alongside, same era.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020b
Cited in the paper.
Experts, errors, and context: A large-scale study of human evaluation for machine translation
Markus Freitag, George Foster, David Grangier, Viresh Ratnakar, Qijun Tan, and Wolfgang Macherey. 2021a
Cited in the paper.
Results of WMT22 metrics shared task: Stop using BLEU – neural metrics are better and more robust
Markus Freitag, Ricardo Rei, Nitika Mathur, Chi-kiu Lo, Craig Stewart, Eleftherios Avramidis, Tom Kocmi, George Foster, Alon Lavie, and André F. T. Martins. 2022a
Cited in the paper.
Results of wmt22 metrics shared task: Stop using bleu neural metrics are better and more robust
Markus Freitag, Ricardo Rei, Nitika Mathur, Chi-kiu Lo, Craig Stewart, Eleftherios Avramidis, Tom Kocmi, George Foster, Alon Lavie, and Andre F. T. Martins. 2022b
Cited in the paper.
Results of the WMT21 metrics shared task: Evaluating metrics with expert-based human evaluations on TED and news domain
Markus Freitag, Ricardo Rei, Nitika Mathur, Chi-kiu Lo, Craig Stewart, George Foster, Alon Lavie, and Ondřej Bojar. 2021b
Cited in the paper.
Qingyu Lu, Baopu Qiu, Liang Ding, Liping Xie, and Dacheng Tao. 2023 · 2023
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OpenAI. 2023 · 2023
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