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Automatic detection of (potential) factors in the source text leading to gender bias in machine translation

Janica Hackenbuchner (UGent) , Arda Tezcan (UGent) and Joke Daems (UGent)
(2024) Proceedings of the 25th Annual Conference of the European Association for Machine Translation, volume 2 : products & projects. p.27-28
Author
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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.

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MLA
Hackenbuchner, Janica, et al. “Automatic Detection of (Potential) Factors in the Source Text Leading to Gender Bias in Machine Translation.” Proceedings of the 25th Annual Conference of the European Association for Machine Translation, Volume 2 : Products & Projects, edited by Carolina Scarton et al., European Association for Machine Translation (EAMT), 2024, pp. 27–28.
APA
Hackenbuchner, J., Tezcan, A., & Daems, J. (2024). Automatic detection of (potential) factors in the source text leading to gender bias in machine translation. In C. Scarton, C. Prescott, C. Bayliss, C. Oakley, J. Wright, S. Wrigley, … H. Moniz (Eds.), Proceedings of the 25th Annual Conference of the European Association for Machine Translation, volume 2 : products & projects (pp. 27–28). European Association for Machine Translation (EAMT).
Chicago author-date
Hackenbuchner, Janica, Arda Tezcan, and Joke Daems. 2024. “Automatic Detection of (Potential) Factors in the Source Text Leading to Gender Bias in Machine Translation.” In Proceedings of the 25th Annual Conference of the European Association for Machine Translation, Volume 2 : Products & Projects, edited by Carolina Scarton, Charlotte Prescott, Chris Bayliss, Chris Oakley, Joanna Wright, Stuart Wrigley, Xingyi Song, Edward Gow-Smith, Mikel Forcada, and Helena Moniz, 27–28. European Association for Machine Translation (EAMT).
Chicago author-date (all authors)
Hackenbuchner, Janica, Arda Tezcan, and Joke Daems. 2024. “Automatic Detection of (Potential) Factors in the Source Text Leading to Gender Bias in Machine Translation.” In Proceedings of the 25th Annual Conference of the European Association for Machine Translation, Volume 2 : Products & Projects, ed by. Carolina Scarton, Charlotte Prescott, Chris Bayliss, Chris Oakley, Joanna Wright, Stuart Wrigley, Xingyi Song, Edward Gow-Smith, Mikel Forcada, and Helena Moniz, 27–28. European Association for Machine Translation (EAMT).
Vancouver
1.
Hackenbuchner J, Tezcan A, Daems J. Automatic detection of (potential) factors in the source text leading to gender bias in machine translation. In: Scarton C, Prescott C, Bayliss C, Oakley C, Wright J, Wrigley S, et al., editors. Proceedings of the 25th Annual Conference of the European Association for Machine Translation, volume 2 : products & projects. European Association for Machine Translation (EAMT); 2024. p. 27–8.
IEEE
[1]
J. Hackenbuchner, A. Tezcan, and J. Daems, “Automatic detection of (potential) factors in the source text leading to gender bias in machine translation,” in Proceedings of the 25th Annual Conference of the European Association for Machine Translation, volume 2 : products & projects, Sheffield, UK, 2024, pp. 27–28.
@inproceedings{01HXVKP4TPYGBZAPGAYCVS82JC,
  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.}},
  author       = {{Hackenbuchner, Janica and Tezcan, Arda and Daems, Joke}},
  booktitle    = {{Proceedings of the 25th Annual Conference of the European Association for Machine Translation, volume 2 : products & projects}},
  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}},
  isbn         = {{9781068690716}},
  language     = {{eng}},
  location     = {{Sheffield, UK}},
  pages        = {{27--28}},
  publisher    = {{European Association for Machine Translation (EAMT)}},
  title        = {{Automatic detection of (potential) factors in the source text leading to gender bias in machine translation}},
  url          = {{https://aclanthology.org/2024.eamt-2.14}},
  year         = {{2024}},
}