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While machine translation (MT) systems have seen significant improvements, it is still common for translations to reflect societal biases, such as gender bias.
chrF: character n-gram F-score for automatic MT evaluation
Maja Popović. 2015 · 2015
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Assessing gender bias in machine translation - A case study with google translate
Marcelo O. R. Prates, Pedro H. C. Avelar, and Luís C. Lamb. 2018 · 2018
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Equalizing gender bias in neural machine translation with word embeddings techniques
Joel Escudé Font and Marta R. Costa-jussà. 2019 · 2019
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Two new evaluation datasets for low-resource machine translation: Nepali-english and sinhala-english
Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, and Marc’Aurelio Ranzato. 2019 · 2019
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Evaluating gender bias in machine translation
Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer. 2019 · 2019
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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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Reducing gender bias in neural machine translation as a domain adaptation problem
Danielle Saunders and Bill Byrne. 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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Mitigating gender bias in machine translation with target gender annotations
Artūrs Stafanovičs, Toms Bergmanis, and Mārcis Pinnis. 2020 · 2020
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Collecting a large-scale gender bias dataset for coreference resolution and machine translation
Shahar Levy, Koren Lazar, and Gabriel Stanovsky. 2021 · 2021
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Palm: Scaling language modeling with pathways
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Interpreting gender bias in neural machine translation: Multilingual architecture matters
Marta R. Costa-jussà, Carlos Escolano, Christine Basta, Javier Ferrando, Roser Batlle, and Ksenia Kharitonova. 2022 · 2022
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Mitigating gender bias in machine translation through adversarial learning
Eve Fleisig and Christiane Fellbaum. 2022 · 2022
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A taxonomy of bias-causing ambiguities in machine translation
Michal Měchura. 2022 · 2022
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No language left behind: Scaling human-centered machine translation
NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang. 2022 · 2022
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Hallucinations in large multilingual translation models
Nuno M. Guerreiro, Duarte Alves, Jonas Waldendorf, Barry Haddow, Alexandra Birch, Pierre Colombo, and André F. T. Martins. 2023 · 2023
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How good are gpt models at machine translation? a comprehensive evaluation
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Is chatgpt a good translator? yes with gpt-4 as the engine
Wenxiang Jiao, Wenxuan Wang, Jen tse Huang, Xing Wang, and Zhaopeng Tu. 2023 · 2023
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A scalable approach to reducing gender bias in google translate
Melvin Johnson. 2020 · 2023
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Findings of the 2023 conference on machine translation (WMT23): LLMs are here but not quite there yet
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“I’m sorry to hear that”: Finding new biases in language models with a holistic descriptor dataset
Eric Michael Smith, Melissa Hall, Melanie Kambadur, Eleonora Presani, and Adina Williams. 2022 · 2022
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Measuring and mitigating name biases in neural machine translation
Jun Wang, Benjamin Rubinstein, and Trevor Cohn. 2022 · 2022
Cited alongside, same era.
In-context examples selection for machine translation
Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad. 2023 · 2023
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BLASER: A text-free speech-to-speech translation evaluation metric
Mingda Chen, Paul-Ambroise Duquenne, Pierre Andrews, Justine Kao, Alexandre Mourachko, Holger Schwenk, and Marta R. Costa-jussà. 2023 · 2023
Cited alongside, same era.
Marta R. Costa-jussà, Pierre Andrews, Eric Smith, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Daniel Licht, and Carleigh Wood. 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, Fangxiaoyu Feng, Melvin Johnson, and Orhan Firat. 2023 · 2023
Cited alongside, same era.
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 Guzmán, and Angela Fan. 2021a
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Llama 2: Open foundation and fine-tuned chat models
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Prompting large language model for machine translation: A case study
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Multilingual machine translation with large language models: Empirical results and analysis
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