| Challenge: | Interest in understanding, assessing, and mitigating gender bias in machine translation (MT) still lacks cohesion. |
| Approach: | They propose to review current conceptualizations of gender bias in machine translation (MT) they summarize previous studies and propose ways to mitigate bias. |
| Outcome: | This paper summarizes the current conceptualizations and proposes strategies to mitigate biases in machine translation (MT) . |
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| Challenge: | Using morphological analysis, we find that MT models exhibit gender-biased translation errors when training data encode stereotypes not relevant for the task. |
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What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study (2024.emnlp-main)
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| Challenge: | Existing bias measurements do not reflect the gender disparities found in machine translation. |
| Approach: | They conduct a human-centered study to examine if and to what extent bias in machine translation brings harms with tangible costs, such as quality of service gaps between women and men. |
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Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus (2020.acl-main)
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| Challenge: | a growing number of studies have examined the issue of gender bias in speech translation . a gender bias is a systemic problem that reproduces gender stereotypes discriminating women. |
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Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
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Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
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Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Terms (2025.emnlp-main)
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| Challenge: | ailsntua researchers examine whether machine translation systems exhibit gender biases that reinforce societal stereotypes. |
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