“You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases (2020.acl-main)
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| Challenge: | a recent study shows that machine translations make older and more male characters sound older and older than the original. |
| Approach: | They propose to use demographicallyrepresentative data to examine how text is translated . they show that authors sound older and more male than the original . |
| Outcome: | The results suggest that translation models reflect demographic bias in the training data. |
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| Challenge: | a study conducted on Icelandic translations in the translation systems Google Translate and Véling.is . results show a pattern which corresponds to certain societal ideas about gender. |
| Approach: | They examine how gender bias appears in English-Icelandic translations . they conducted a study on Icelandic translation in the translation systems Google Translate and Véling.is . |
| Outcome: | The main purpose of the study is to examine how gender bias appears in English-Icelandic translations. |
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation (2021.eacl-main)
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| Challenge: | Existing studies have shown that existing models amplify biases observed in training data. |
| Approach: | They propose to use MT and NLP to amplify biases observed in training data to investigate how bias amplification might affect language in a broader sense. |
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What about “em”? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns (2023.acl-long)
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| Challenge: | Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals. |
| Approach: | They compare 3rd-person pronoun translations to five other languages . they propose to address gender exclusivity in future research . |
| Outcome: | The proposed method compares translations of gendered vs. gender-neutral pronouns from english to five other languages and vice versa. |
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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Different Speech Translation Models Encode and Translate Speaker Gender Differently (2025.acl-short)
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| Challenge: | Recent studies on interpreting the hidden states of speech models have shown their ability to capture speaker-specific features, including gender. |
| Approach: | They propose to use probing methods to assess gender encoding across ST models. |
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Translate With Care: Addressing Gender Bias, Neutrality, and Reasoning in Large Language Model Translations (2025.findings-acl)
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| Challenge: | addressing gender bias and maintaining logical coherence in machine translation remains challenging, especially when translating between natural gender languages, like English, and genderless languages, such as Persian, Indonesian, and Finnish. |
| Approach: | They propose a dataset to assess translation systems' performance in six low- to mid-resource languages and a translation dataset to examine gender bias and logical coherence. |
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Does Context Help Mitigate Gender Bias in Neural Machine Translation? (2024.findings-emnlp)
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| Challenge: | Neural machine translation models perpetuate gender bias in their training data distribution. |
| Approach: | They examine the gender bias in Neural Machine Translation by using context-aware models to enhance translation accuracy for feminine terms and translation with non-informative context in Basque to Spanish. |
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How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contexts. (2022.findings-emnlp)
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| Challenge: | Neural Machine Translation systems are prone to gender biases in their learned representations. |
| Approach: | They propose to use contextual sentences to correct gender bias in Neural Machine Translation models. |
| Outcome: | The proposed method can be used to build better, bias-free translation systems. |
Gender Bias in Machine Translation (2021.tacl-1)
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| 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. |
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Bias and Fairness in Natural Language Processing (D19-2)
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| Challenge: | a tutorial will review the history of bias and fairness studies in machine learning and language processing . |
| Approach: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it presents recent community effort to quantify and mitigat bias in natural language processing models . |
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