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As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased.
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Eva Vanmassenhove, Christian Hardmeier, and Andy Way. 2018 · 2018
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Findings of the 2019 conference on machine translation (WMT19)
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When and why is document-level context useful in neural machine translation?
Yunsu Kim, Duc Thanh Tran, and Hermann Ney. 2019 · 2019
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The IWSLT 2019 evaluation campaign
Jan Niehues, Rolando Cattoni, Sebastian Stüker, Matteo Negri, Marco Turchi, Thanh-Le Ha, Elizabeth Salesky, Ramon Sanabria, Loic Barrault, Lucia Specia, and Marcello Federico. 2019 · 2019
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Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer. 2019 · 2019
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Mitigating gender bias in natural language processing: Literature review
Reducing gender bias in neural machine translation as a domain adaptation problem
Danielle Saunders and Bill Byrne. 2020 · 2020
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Neural machine translation doesn’t translate gender coreference right unless you make it
Danielle Saunders, Rosie Sallis, and Bill Byrne. 2020 · 2020
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MuST-C: A multilingual corpus for end-to-end speech translation
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GFST: Gender-filtered self-training for more accurate gender in translation
Prafulla Kumar Choubey, Anna Currey, Prashant Mathur, and Georgiana Dinu. 2021 · 2021
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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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Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang. 2019 · 2019
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Towards mitigating gender bias in a decoder-based neural machine translation model by adding contextual information
Christine Basta, Marta R. Costa-jussà, and José A. R. Fonollosa. 2020 · 2020
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Gender in danger? evaluating speech translation technology on the MuST-SHE corpus
Luisa Bentivogli, Beatrice Savoldi, Matteo Negri, Mattia A. Di Gangi, Roldano Cattoni, and Marco Turchi. 2020 · 2020
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Language (technology) is power: A critical survey of “bias” in NLP
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GeBioToolkit: Automatic extraction of gender-balanced multilingual corpus of Wikipedia biographies
Marta R. Costa-jussà, Pau Li Lin, and Cristina España-Bonet. 2020 · 2020
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“you sound just like your father” commercial machine translation systems include stylistic biases
Dirk Hovy, Federico Bianchi, and Tommaso Fornaciari. 2020 · 2020
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Gender coreference and bias evaluation at WMT 2020
Tom Kocmi, Tomasz Limisiewicz, and Gabriel Stanovsky. 2020 · 2020
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Gender bias amplification during speed-quality optimization in neural machine translation
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Gender bias in machine translation
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Extending challenge sets to uncover gender bias in machine translation: Impact of stereotypical verbs and adjectives
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gENder-IT: An annotated English-Italian parallel challenge set for cross-linguistic natural gender phenomena
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Sockeye 3: Fast neural machine translation with "pytorch"
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A baseline revisited: Pushing the limits of multi-segment models for context-aware translation
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CoCoA-MT: A dataset and benchmark for contrastive controlled MT with application to formality
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Under the morphosyntactic lens: A multifaceted evaluation of gender bias in speech translation
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