Machine Translation (MT) continues to improve in quality and adoption, yet the inadvertent perpetuation of gender bias remains a significant concern. Despite numerous studies into gender bias in translations from gender-neutral languages such as Turkish into more strongly gendered languages like English, there are no benchmarks for evaluating this phenomenon or for assessing mitigation strategies. To address this gap, we introduce GATE X-E, an extension to the GATE (Rarrick et al., 2023) corpus, that consists of human translations from Turkish, Hungarian, Finnish, and Persian into English. Each translation is accompanied by feminine, masculine, and neutral variants for each possible gender interpretation. The dataset, which contains between 1250 and 1850 instances for each of the four language pairs, features natural sentences with a wide range of sentence lengths and domains, challenging translation rewriters on various linguistic phenomena. Additionally, we present an English gender rewriting solution built on GPT-3.5 Turbo and use GATE X-E to evaluate it. We open source our contributions to encourage further research on gender debiasing.

翻译：机器翻译（MT）的质量与普及度持续提升，但无意识延续的性别偏见仍是显著问题。尽管已有大量研究关注从土耳其语等中性语言向英语等强性别语言翻译中的性别偏见现象，但评估该现象及其缓解策略的基准数据集仍然缺失。为填补这一空白，我们提出GATE X-E数据集（基于Rarrick等人2023年GATE语料库的扩展版），包含土耳其语、匈牙利语、芬兰语和波斯语到英语的人工翻译。每个翻译结果针对每种可能的性别解读提供对应的阴性、阳性和中性变体。该数据集包含四种语言对的1250至1850个实例，涵盖不同长度和领域的自然语句，挑战翻译改写器对多种语言现象的处理能力。此外，我们构建了一个基于GPT-3.5 Turbo的英语性别改写方案，并利用GATE X-E进行评估。我们将相关成果开源，以推动性别去偏见研究的进一步发展。