Computer Science > Computation and Language
arXiv:2407.20438 (cs)
[Submitted on 29 Jul 2024]
Title:Generating Gender Alternatives in Machine Translation
View a PDF of the paper titled Generating Gender Alternatives in Machine Translation, by Sarthak Garg and 5 other authors
View PDFHTML (experimental)Abstract: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.
| Comments: | GeBNLP 2024 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2407.20438 [cs.CL] |
| (orarXiv:2407.20438v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2407.20438 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Generating Gender Alternatives in Machine Translation, by Sarthak Garg and 5 other authors
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