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

Janica Hackenbuchner (UGent) , Joke Daems (UGent) and Arda Tezcan (UGent)
(2024) 25th Annual Conference of the European Association for Machine Translation, Abstracts.
Author
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Project
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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Citation

Please use this url to cite or link to this publication:

MLA
Hackenbuchner, Janica, et al. “Automatic Detection of (Potential) Factors in the Source Text Leading to Gender Bias in Machine Translation.” 25th Annual Conference of the European Association for Machine Translation, Abstracts, 2024.
APA
Hackenbuchner, J., Daems, J., & Tezcan, A. (2024). Automatic detection of (potential) factors in the source text leading to gender bias in machine translation. 25th Annual Conference of the European Association for Machine Translation, Abstracts. Presented at the 25th Annual Conference of the European Association for Machine Translation (EAMT 2024), Sheffield, UK.
Chicago author-date
Hackenbuchner, Janica, Joke Daems, and Arda Tezcan. 2024. “Automatic Detection of (Potential) Factors in the Source Text Leading to Gender Bias in Machine Translation.” In 25th Annual Conference of the European Association for Machine Translation, Abstracts.
Chicago author-date (all authors)
Hackenbuchner, Janica, Joke Daems, and Arda Tezcan. 2024. “Automatic Detection of (Potential) Factors in the Source Text Leading to Gender Bias in Machine Translation.” In 25th Annual Conference of the European Association for Machine Translation, Abstracts.
Vancouver
1.
Hackenbuchner J, Daems J, Tezcan A. Automatic detection of (potential) factors in the source text leading to gender bias in machine translation. In: 25th Annual Conference of the European Association for Machine Translation, Abstracts. 2024.
IEEE
[1]
J. Hackenbuchner, J. Daems, and A. Tezcan, “Automatic detection of (potential) factors in the source text leading to gender bias in machine translation,” in 25th Annual Conference of the European Association for Machine Translation, Abstracts, Sheffield, UK, 2024.
@inproceedings{01K94WRE39HV7CEA6K1MBA0CWN,
  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 Daems, Joke and Tezcan, Arda}},
  booktitle    = {{25th Annual Conference of the European Association for Machine Translation, Abstracts}},
  language     = {{eng}},
  location     = {{Sheffield, UK}},
  title        = {{Automatic detection of (potential) factors in the source text leading to gender bias in machine translation}},
  url          = {{https://eamt2024.sheffield.ac.uk/programme}},
  year         = {{2024}},
}