@inproceedings{lardelli-gromann-2023-gender,
title = "Gender-Fair Post-Editing: A Case Study Beyond the Binary",
author = "Lardelli, Manuel and
Gromann, Dagmar",
editor = "Nurminen, Mary and
Brenner, Judith and
Koponen, Maarit and
Latomaa, Sirkku and
Mikhailov, Mikhail and
Schierl, Frederike and
Ranasinghe, Tharindu and
Vanmassenhove, Eva and
Vidal, Sergi Alvarez and
Aranberri, Nora and
Nunziatini, Mara and
Escart{\'\i}n, Carla Parra and
Forcada, Mikel and
Popovic, Maja and
Scarton, Carolina and
Moniz, Helena",
booktitle = "Proceedings of the 24th Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2023",
address = "Tampere, Finland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2023.eamt-1.24",
pages = "251--260",
abstract = "Machine Translation (MT) models are well-known to suffer from gender bias, especially for gender beyond a binary conception. Due to the multiplicity of language-specific strategies for gender representation beyond the binary, debiasing MT is extremely challenging. As an alternative, we propose a case study on gender-fair post-editing. In this study, six professional translators each post-edited three English to German machine translations. For each translation, participants were instructed to use a different gender-fair language strategy, that is, gender-neutral rewording, gender-inclusive characters, and a neosystem. The focus of this study is not on translation quality but rather on the ease of integrating gender-fair language into the post-editing process. Findings from non-participant observation and interviews show clear differences in temporal and cognitive effort between participants and strategy as well as in the success of using gender-fair language.",
}
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<abstract>Machine Translation (MT) models are well-known to suffer from gender bias, especially for gender beyond a binary conception. Due to the multiplicity of language-specific strategies for gender representation beyond the binary, debiasing MT is extremely challenging. As an alternative, we propose a case study on gender-fair post-editing. In this study, six professional translators each post-edited three English to German machine translations. For each translation, participants were instructed to use a different gender-fair language strategy, that is, gender-neutral rewording, gender-inclusive characters, and a neosystem. The focus of this study is not on translation quality but rather on the ease of integrating gender-fair language into the post-editing process. Findings from non-participant observation and interviews show clear differences in temporal and cognitive effort between participants and strategy as well as in the success of using gender-fair language.</abstract>
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%0 Conference Proceedings
%T Gender-Fair Post-Editing: A Case Study Beyond the Binary
%A Lardelli, Manuel
%A Gromann, Dagmar
%Y Nurminen, Mary
%Y Brenner, Judith
%Y Koponen, Maarit
%Y Latomaa, Sirkku
%Y Mikhailov, Mikhail
%Y Schierl, Frederike
%Y Ranasinghe, Tharindu
%Y Vanmassenhove, Eva
%Y Vidal, Sergi Alvarez
%Y Aranberri, Nora
%Y Nunziatini, Mara
%Y Escartín, Carla Parra
%Y Forcada, Mikel
%Y Popovic, Maja
%Y Scarton, Carolina
%Y Moniz, Helena
%S Proceedings of the 24th Annual Conference of the European Association for Machine Translation
%D 2023
%8 June
%I European Association for Machine Translation
%C Tampere, Finland
%F lardelli-gromann-2023-gender
%X Machine Translation (MT) models are well-known to suffer from gender bias, especially for gender beyond a binary conception. Due to the multiplicity of language-specific strategies for gender representation beyond the binary, debiasing MT is extremely challenging. As an alternative, we propose a case study on gender-fair post-editing. In this study, six professional translators each post-edited three English to German machine translations. For each translation, participants were instructed to use a different gender-fair language strategy, that is, gender-neutral rewording, gender-inclusive characters, and a neosystem. The focus of this study is not on translation quality but rather on the ease of integrating gender-fair language into the post-editing process. Findings from non-participant observation and interviews show clear differences in temporal and cognitive effort between participants and strategy as well as in the success of using gender-fair language.
%U https://aclanthology.org/2023.eamt-1.24
%P 251-260
Markdown (Informal)
[Gender-Fair Post-Editing: A Case Study Beyond the Binary](https://aclanthology.org/2023.eamt-1.24) (Lardelli & Gromann, EAMT 2023)
ACL