Generating Gender Alternatives in Machine Translation

• URL: http://arxiv.org/abs/2407.20438v1
• Date: Mon, 29 Jul 2024 22:10:51 GMT
• Title: Generating Gender Alternatives in Machine Translation
• Authors: Sarthak Garg, Mozhdeh Gheini, Clara Emmanuel, Tatiana Likhomanenko, Qin Gao, Matthias Paulik,
• Abstract summary: Machine translation systems often translate terms with ambiguous gender into the gendered form that is most prevalent in the systems' training data. This often reflects and perpetuates harmful stereotypes present in society. We study the problem of generating all grammatically correct gendered translation alternatives.
• Score: 13.153018685139413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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