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.

机器翻译在质量和应用方面不断取得进步，然而无意中传递性别偏见仍然是一个重大问题。为了弥补这一缺口，我们介绍了 GATE X-E，它是 GATE 语料库的扩展，包含了从土耳其语、匈牙利语、芬兰语和波斯语翻译成英语的人工翻译，并针对每种可能的性别解释提供了女性、男性和中性变体。我们还呈现了一个基于 GPT-3.5 Turbo 的英语性别重写解决方案，并利用 GATE X-E 对其进行了评估。我们开放源代码以鼓励进一步研究性别去偏见。