@inproceedings{hackenbuchner-etal-2024-automatic,
title = "Automatic detection of (potential) factors in the source text leading to gender bias in machine translation",
author = "Hackenbuchner, Jani{\c{c}}a and
Tezcan, Arda and
Daems, Joke",
editor = "Scarton, Carolina and
Prescott, Charlotte and
Bayliss, Chris and
Oakley, Chris and
Wright, Joanna and
Wrigley, Stuart and
Song, Xingyi and
Gow-Smith, Edward and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)",
month = jun,
year = "2024",
address = "Sheffield, UK",
publisher = "European Association for Machine Translation (EAMT)",
url = "https://aclanthology.org/2024.eamt-2.14",
pages = "27--28",
abstract = "This research project aims to develop a comprehensive methodology to help make machine translation (MT) systems more gender-inclusive for society. The goal is the creation of a detection system, a machine learning (ML) model trained on manual annotations, that can automatically analyse source data and detect and highlight words and phrases that influence the gender bias inflection in target translations.The main research outputs will be (1) a manually annotated dataset, (2) a taxonomy, and (3) a fine-tuned model.",
}
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<abstract>This research project aims to develop a comprehensive methodology to help make machine translation (MT) systems more gender-inclusive for society. The goal is the creation of a detection system, a machine learning (ML) model trained on manual annotations, that can automatically analyse source data and detect and highlight words and phrases that influence the gender bias inflection in target translations.The main research outputs will be (1) a manually annotated dataset, (2) a taxonomy, and (3) a fine-tuned model.</abstract>
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%0 Conference Proceedings
%T Automatic detection of (potential) factors in the source text leading to gender bias in machine translation
%A Hackenbuchner, Janiça
%A Tezcan, Arda
%A Daems, Joke
%Y Scarton, Carolina
%Y Prescott, Charlotte
%Y Bayliss, Chris
%Y Oakley, Chris
%Y Wright, Joanna
%Y Wrigley, Stuart
%Y Song, Xingyi
%Y Gow-Smith, Edward
%Y Forcada, Mikel
%Y Moniz, Helena
%S Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)
%D 2024
%8 June
%I European Association for Machine Translation (EAMT)
%C Sheffield, UK
%F hackenbuchner-etal-2024-automatic
%X This research project aims to develop a comprehensive methodology to help make machine translation (MT) systems more gender-inclusive for society. The goal is the creation of a detection system, a machine learning (ML) model trained on manual annotations, that can automatically analyse source data and detect and highlight words and phrases that influence the gender bias inflection in target translations.The main research outputs will be (1) a manually annotated dataset, (2) a taxonomy, and (3) a fine-tuned model.
%U https://aclanthology.org/2024.eamt-2.14
%P 27-28
Markdown (Informal)
[Automatic detection of (potential) factors in the source text leading to gender bias in machine translation](https://aclanthology.org/2024.eamt-2.14) (Hackenbuchner et al., EAMT 2024)
ACL