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. |
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| Challenge: | Current modeling of 3rd person pronouns ignores neopronoun phenomena like naive pronounes, which are not (yet) widely established. |
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MISGENDERED: Limits of Large Language Models in Understanding Pronouns (2023.acl-long)
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| Challenge: | excluding non-binary gender identities can perpetuate harm against non-bisexual individuals through exclusion and marginalization. |
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A Survey on Zero Pronoun Translation (2023.acl-long)
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| Challenge: | Recent instruction fine-tuned models can solve multiple NLP tasks when prompted to do so, but current research focuses on standard performance benchmarks, leaving compelling fairness and ethical considerations behind. |
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Gender Bias in Machine Translation (2021.tacl-1)
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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. |
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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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