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Gender inequality is embedded in our communication practices and perpetuatedin translation technologies. This becomes particularly apparent whentranslating into grammatical gender languages, where machine translation (MT)often defaults to masculine and stereotypical representations by making unduebinary gender assumptions. Our work addresses the rising demand for inclusivelanguage by focusing head-on on gender-neutral translation from English toItalian. We start from the essentials: proposing a dedicated benchmark andexploring automated evaluation methods. First, we introduce GeNTE, a natural,bilingual test set for gender-neutral translation, whose creation was informedby a survey on the perception and use of neutral language. Based on GeNTE, wethen overview existing reference-based evaluation approaches, highlight theirlimits, and propose a reference-free method more suitable to assessgender-neutral translation.

Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus