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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2407.20438 |
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| _version_ | 1866911971486466048 |
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| author | Garg, Sarthak Gheini, Mozhdeh Emmanuel, Clara Likhomanenko, Tatiana Gao, Qin Paulik, Matthias |
| author_facet | Garg, Sarthak Gheini, Mozhdeh Emmanuel, Clara Likhomanenko, Tatiana Gao, Qin Paulik, Matthias |
| contents | Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term "the nurse") into the gendered form that is most prevalent in the systems' training data (e.g., "enfermera", the Spanish term for a female nurse). This often reflects and perpetuates harmful stereotypes present in society. With MT user interfaces in mind that allow for resolving gender ambiguity in a frictionless manner, we study the problem of generating all grammatically correct gendered translation alternatives. We open source train and test datasets for five language pairs and establish benchmarks for this task. Our key technical contribution is a novel semi-supervised solution for generating alternatives that integrates seamlessly with standard MT models and maintains high performance without requiring additional components or increasing inference overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20438 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generating Gender Alternatives in Machine Translation Garg, Sarthak Gheini, Mozhdeh Emmanuel, Clara Likhomanenko, Tatiana Gao, Qin Paulik, Matthias Computation and Language Artificial Intelligence Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term "the nurse") into the gendered form that is most prevalent in the systems' training data (e.g., "enfermera", the Spanish term for a female nurse). This often reflects and perpetuates harmful stereotypes present in society. With MT user interfaces in mind that allow for resolving gender ambiguity in a frictionless manner, we study the problem of generating all grammatically correct gendered translation alternatives. We open source train and test datasets for five language pairs and establish benchmarks for this task. Our key technical contribution is a novel semi-supervised solution for generating alternatives that integrates seamlessly with standard MT models and maintains high performance without requiring additional components or increasing inference overhead. |
| title | Generating Gender Alternatives in Machine Translation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2407.20438 |