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Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT).
Automatic post-editing for machine translation
Rajen Chatterjee. 2019 · 1910
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Learning non-monotonic automatic post-editing of translations from human orderings
António Góis, Kyunghyun Cho, and André Martins. 2020 · 2004
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
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Automatic post-editing of machine translation: A neural programmer-interpreter approach
Thuy-Trang Vu and Gholamreza Haffari. 2018 · 2018
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A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
Gonçalo M. Correia and André F. T. Martins. 2019 · 2019
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Context-aware monolingual repair for neural machine translation
Elena Voita, Rico Sennrich, and Ivan Titov. 2019 · 2019
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Can automatic post-editing improve nmt?
Shamil Chollampatt, Raymond Susanto, Liling Tan, and Ewa Szymanska. 2020 · 2020
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A review of the state-of-the-art in automatic post-editing
Félix do Carmo, D. Shterionov, Joss Moorkens, Joachim Wagner, Murhaf Hossari, Eric Paquin, Dag Schmidtke, Declan Groves, and Andy Way. 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 P Parikh. 2020 · 2020
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A roadmap to neural automatic post-editing: an empirical approach
Dimitar Shterionov, Félix do Carmo, Joss Moorkens, Murhaf Hossari, Joachim Wagner, Eric Paquin, Dag Schmidtke, Declan Groves, and Andy Way. 2020 · 2020
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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. 2021 · 2021
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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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No language left behind: Scaling human-centered machine translation
Marta R Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, et al. 2022 · 2022
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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. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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MaTESe: Machine translation evaluation as a sequence tagging problem
Stefano Perrella, Lorenzo Proietti, Alessandro Scirè, Niccolò Campolungo, and Roberto Navigli. 2022 · 2022
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Cometkiwi: Ist-unbabel 2022 submission for the quality estimation shared task
Ricardo Rei, Marcos Treviso, Nuno M Guerreiro, Chrysoula Zerva, Ana C Farinha, Christine Maroti, José GC De Souza, Taisiya Glushkova, Duarte Alves, Luísa Coheur, et al. 2022 · 2022
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Self-critiquing models for assisting human evaluators
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
In-context examples selection for machine translation
Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad. 2023 · 2023
Cited alongside, same era.
Iterative translation refinement with large language models
Pinzhen Chen, Zhicheng Guo, Barry Haddow, and Kenneth Heafield. 2023 · 2023
Cited alongside, same era.
The devil is in the errors: Leveraging large language models for fine-grained machine translation evaluation
Qingyu Lu, Baopu Qiu, Liang Ding, Kanjian Zhang, Tom Kocmi, and Dacheng Tao. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2023 · 2023
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Adaptive machine translation with large language models
Yasmin Moslem, Rejwanul Haque, John D. Kelleher, and Andy Way. 2023 · 2023
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Liangming Pan, Michael Saxon, Wenda Xu, Deepak Nathani, Xinyi Wang, and William Yang Wang. 2023 · 2023
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Towards making the most of ChatGPT for machine translation
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Patrick Fernandes, Daniel Deutsch, Mara Finkelstein, Parker Riley, André FT Martins, Graham Neubig, Ankush Garg, Jonathan H Clark, Markus Freitag, and Orhan Firat. 2023 · 2023
Cited alongside, same era.
Unleashing the power of chatgpt for translation: An empirical study
Yuan Gao, Ruili Wang, and Feng Hou. 2023 · 2023
Cited alongside, same era.
The unreasonable effectiveness of few-shot learning for machine translation
Xavier Garcia, Yamini Bansal, Colin Cherry, George Foster, Maxim Krikun, Melvin Johnson, and Orhan Firat. 2023 · 2023
Cited alongside, same era.
Cometoid: Distilling strong reference-based machine translation metrics into Even stronger quality estimation metrics
Thamme Gowda, Tom Kocmi, and Marcin Junczys-Dowmunt. 2023 · 2023
Cited alongside, same era.
xcomet: Transparent machine translation evaluation through fine-grained error detection
Nuno M Guerreiro, Ricardo Rei, Daan van Stigt, Luisa Coheur, Pierre Colombo, and André FT Martins. 2023 · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Is chatgpt a good translator? a preliminary study
Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, and Zhaopeng Tu. 2023 · 2023
Cited alongside, same era.
MetricX-23: The Google submission to the WMT 2023 metrics shared task
Juraj Juraska, Mara Finkelstein, Daniel Deutsch, Aditya Siddhant, Mehdi Mirzazadeh, and Markus Freitag. 2023 · 2023
Cited alongside, same era.
Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, and Dacheng Tao. 2023a · 2023
Later among the works it cites.
Leveraging gpt-4 for automatic translation post-editing
Vikas Raunak, Amr Sharaf, Hany Hassan Awadallah, and Arul Menezes. 2023 · 2023
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R Narasimhan, and Shunyu Yao. 2023 · 2023
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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. 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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Exploring prompt engineering with GPT language models for document-level machine translation: Insights and findings
Yangjian Wu and Gang Hu. 2023 · 2023
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Wen Yang, Chong Li, Jiajun Zhang, and Chengqing Zong. 2023 · 2023
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Tim: Teaching large language models to translate with comparison
Jiali Zeng, Fandong Meng, Yongjing Yin, and Jie Zhou. 2023 · 2023
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Prompting large language model for machine translation: A case study
Biao Zhang, Barry Haddow, and Alexandra Birch. 2023 · 2023
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Multilingual machine translation with large language models: Empirical results and analysis
Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Lingpeng Kong, Jiajun Chen, Lei Li, and Shujian Huang. 2023 · 2023
Later among the works it cites.
Tower: An open multilingual large language model for translation-related tasks
Duarte M Alves, José Pombal, Nuno M Guerreiro, Pedro H Martins, João Alves, Amin Farajian, Ben Peters, Ricardo Rei, Patrick Fernandes, Sweta Agrawal, et al. 2024 · 2024
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A novel paradigm boosting translation capabilities of large language models
Jiaxin Guo, Hao Yang, Zongyao Li, Daimeng Wei, Hengchao Shang, and Xiaoyu Chen. 2024 · 2024
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Zhiwei He, Xing Wang, Wenxiang Jiao, Zhuosheng Zhang, Rui Wang, Shuming Shi, and Zhaopeng Tu. 2024 · 2024
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Adapting large language models for document-level machine translation
Minghao Wu, Thuy-Trang Vu, Lizhen Qu, George Foster, and Gholamreza Haffari. 2024 · 2024
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