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This report provides a preliminary evaluation of ChatGPT for machine translation, including translation prompt, multilingual translation, and translation robustness.
BLEU: A method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
A study of translation edit rate with targeted human annotation
Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006 · 2006
Earlier work this paper cites.
Findings of the 2016 conference on machine translation
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, et al. 2016 · 2016
Earlier work this paper cites.
Neural machine translation with pivot languages
Yong Cheng, Yang Liu, Qian Yang, Maosong Sun, and Wei Xu. 2016 · 2016
Earlier work this paper cites.
Transfer learning for low-resource neural machine translation
Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. 2016 · 2016
Earlier work this paper cites.
Google’s multilingual neural machine translation system: Enabling zero-shot translation
Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda B. Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2017 · 2017
Earlier work this paper cites.
ChrF++: Words helping character n-grams
Maja Popović. 2017 · 2017
Earlier work this paper cites.
MTNT: A testbed for machine translation of noisy text
Paul Michel and Graham Neubig. 2018 · 2018
Earlier work this paper cites.
A call for clarity in reporting bleu scores
Matt Post. 2018 · 2018
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Findings of the WMT 2019 biomedical translation shared task: Evaluation for medline abstracts and biomedical terminologies
Rachel Bawden, Kevin Bretonnel Cohen, Cristian Grozea, Antonio Jimeno Yepes, Madeleine Kittner, Martin Krallinger, Nancy Mah, Aurelie Neveol, Mariana Neves, Felipe Soares, et al. 2019 · 2019
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In neural machine translation, what does transfer learning transfer?
Alham Fikri Aji, Nikolay Bogoychev, Kenneth Heafield, and Rico Sennrich. 2020 · 2020
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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. 2020 · 2020
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Findings of the WMT 2020 shared task on machine translation robustness
Lucia Specia, Zhenhao Li, Juan Pino, Vishrav Chaudhary, Francisco Guzmán, Graham Neubig, Nadir Durrani, Yonatan Belinkov, Philipp Koehn, Hassan Sajjad, et al. 2020 · 2020
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Self-training sampling with monolingual data uncertainty for neural machine translation
Wenxiang Jiao, Xing Wang, Zhaopeng Tu, Shuming Shi, Michael Lyu, and Irwin King. 2021 · 2021
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Tencent ai lab machine translation systems for the WMT21 biomedical translation task
Xing Wang, Zhaopeng Tu, and Shuming Shi. 2021 · 2021
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In-context examples selection for machine translation
Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad. 2022 · 2022
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Tencent AI Lab-Shanghai Jiao Tong University low-resource translation system for the WMT22 translation task
Zhiwei He, Xing Wang, Zhaopeng Tu, Shuming Shi, and Rui Wang. 2022 · 2022
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ConsistTL: Modeling consistency in transfer learning for low-resource neural machine translation
Zhaocong Li, Xuebo Liu, Derek F Wong, Lidia S Chao, and Min Zhang. 2022 · 2022
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Beyond english-centric multilingual machine translation
Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, et al. 2021 · 2021
Cited alongside, same era.
Findings of the 2021 conference on machine translation (WMT21)
Akhbardeh Farhad, Arkhangorodsky Arkady, Biesialska Magdalena, Bojar Ondřej, Chatterjee Rajen, Chaudhary Vishrav, Marta R Costa-jussa, España-Bonet Cristina, Fan Angela, Federmann Christian, et al. 2021 · 2021
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The flores-101 evaluation benchmark for low-resource and multilingual machine translation
Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzman, and Angela Fan. 2021 · 2021
Cited alongside, same era.
Tencent’s multilingual machine translation system for WMT22 large-scale african languages
Wenxiang Jiao, Zhaopeng Tu, Jiarui Li, Wenxuan Wang, Jen-tse Huang, and Shuming Shi. 2022a
Cited in the paper.
Exploiting inactive examples for natural language generation with data rejuvenation
Wenxiang Jiao, Xing Wang, Shilin He, Zhaopeng Tu, Irwin King, and Michael R Lyu. 2022b
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Understanding and improving sequence-to-sequence pretraining for neural machine translation
Wenxuan Wang, Wenxiang Jiao, Yongchang Hao, Xing Wang, Shuming Shi, Zhaopeng Tu, and Michael Lyu. 2022a
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Understanding and mitigating the uncertainty in zero-shot translation
Wenxuan Wang, Wenxiang Jiao, Shuo Wang, Zhaopeng Tu, and Michael R Lyu. 2022b
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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, et al. 2022 · 2022
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GPT-4 technical report
OpenAI. 2023 · 2023
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