DeBiasByUs: Raising Awareness and Creating a Database of MT Bias

Joke Daems; Janiça Hackenbuchner

DeBiasByUs: Raising Awareness and Creating a Database of MT Bias

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Abstract

This paper presents the project initiated by the BiasByUs team resulting from the 2021 Artificially Correct Hackaton. We briefly explain our winning participation in the hackaton, tackling the challenge on ‘Database and detection of gender bi-as in A.I. translations’, we highlight the importance of gender bias in Machine Translation (MT), and describe our pro-posed solution to the challenge, the cur-rent status of the project, and our envi-sioned future collaborations and re-search.

Anthology ID:: 2022.eamt-1.34
Volume:: Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
Month:: June
Year:: 2022
Address:: Ghent, Belgium
Editors:: Helena Moniz, Lieve Macken, Andrew Rufener, Loïc Barrault, Marta R. Costa-jussà, Christophe Declercq, Maarit Koponen, Ellie Kemp, Spyridon Pilos, Mikel L. Forcada, Carolina Scarton, Joachim Van den Bogaert, Joke Daems, Arda Tezcan, Bram Vanroy, Margot Fonteyne
Venue:: EAMT
SIG:
Publisher:: European Association for Machine Translation
Note:
Pages:: 289–290
Language:
URL:: https://aclanthology.org/2022.eamt-1.34/
DOI:
Bibkey:
Cite (ACL):: Joke Daems and Janiça Hackenbuchner. 2022. DeBiasByUs: Raising Awareness and Creating a Database of MT Bias. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 289–290, Ghent, Belgium. European Association for Machine Translation.
Cite (Informal):: DeBiasByUs: Raising Awareness and Creating a Database of MT Bias (Daems & Hackenbuchner, EAMT 2022)
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PDF:: https://aclanthology.org/2022.eamt-1.34.pdf

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@inproceedings{daems-hackenbuchner-2022-debiasbyus,
    title = "{D}e{B}ias{B}y{U}s: Raising Awareness and Creating a Database of {MT} Bias",
    author = "Daems, Joke  and
      Hackenbuchner, Jani{\c{c}}a",
    editor = {Moniz, Helena  and
      Macken, Lieve  and
      Rufener, Andrew  and
      Barrault, Lo{\"i}c  and
      Costa-juss{\`a}, Marta R.  and
      Declercq, Christophe  and
      Koponen, Maarit  and
      Kemp, Ellie  and
      Pilos, Spyridon  and
      Forcada, Mikel L.  and
      Scarton, Carolina  and
      Van den Bogaert, Joachim  and
      Daems, Joke  and
      Tezcan, Arda  and
      Vanroy, Bram  and
      Fonteyne, Margot},
    booktitle = "Proceedings of the 23rd Annual Conference of the European Association for Machine Translation",
    month = jun,
    year = "2022",
    address = "Ghent, Belgium",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2022.eamt-1.34/",
    pages = "289--290",
    abstract = "This paper presents the project initiated by the BiasByUs team resulting from the 2021 Artificially Correct Hackaton. We briefly explain our winning participation in the hackaton, tackling the challenge on `Database and detection of gender bi-as in A.I. translations', we highlight the importance of gender bias in Machine Translation (MT), and describe our pro-posed solution to the challenge, the cur-rent status of the project, and our envi-sioned future collaborations and re-search."
}

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%0 Conference Proceedings
%T DeBiasByUs: Raising Awareness and Creating a Database of MT Bias
%A Daems, Joke
%A Hackenbuchner, Janiça
%Y Moniz, Helena
%Y Macken, Lieve
%Y Rufener, Andrew
%Y Barrault, Loïc
%Y Costa-jussà, Marta R.
%Y Declercq, Christophe
%Y Koponen, Maarit
%Y Kemp, Ellie
%Y Pilos, Spyridon
%Y Forcada, Mikel L.
%Y Scarton, Carolina
%Y Van den Bogaert, Joachim
%Y Daems, Joke
%Y Tezcan, Arda
%Y Vanroy, Bram
%Y Fonteyne, Margot
%S Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
%D 2022
%8 June
%I European Association for Machine Translation
%C Ghent, Belgium
%F daems-hackenbuchner-2022-debiasbyus
%X This paper presents the project initiated by the BiasByUs team resulting from the 2021 Artificially Correct Hackaton. We briefly explain our winning participation in the hackaton, tackling the challenge on ‘Database and detection of gender bi-as in A.I. translations’, we highlight the importance of gender bias in Machine Translation (MT), and describe our pro-posed solution to the challenge, the cur-rent status of the project, and our envi-sioned future collaborations and re-search.
%U https://aclanthology.org/2022.eamt-1.34/
%P 289-290

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Markdown (Informal)

[DeBiasByUs: Raising Awareness and Creating a Database of MT Bias](https://aclanthology.org/2022.eamt-1.34/) (Daems & Hackenbuchner, EAMT 2022)

DeBiasByUs: Raising Awareness and Creating a Database of MT Bias (Daems & Hackenbuchner, EAMT 2022)

ACL

Joke Daems and Janiça Hackenbuchner. 2022. DeBiasByUs: Raising Awareness and Creating a Database of MT Bias. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 289–290, Ghent, Belgium. European Association for Machine Translation.