Gender inequality is embedded in our communication practices and perpetuated
in translation technologies. This becomes particularly apparent when
translating into grammatical gender languages, where machine translation (MT)
often defaults to masculine and stereotypical representations by making undue
binary gender assumptions. Our work addresses the rising demand for inclusive
language by focusing head-on on gender-neutral translation from English to
Italian. We start from the essentials: proposing a dedicated benchmark and
exploring automated evaluation methods. First, we introduce GeNTE, a natural,
bilingual test set for gender-neutral translation, whose creation was informed
by a survey on the perception and use of neutral language. Based on GeNTE, we
then overview existing reference-based evaluation approaches, highlight their
limits, and propose a reference-free method more suitable to assess
gender-neutral translation.

我们的研究主要关注从英语到意大利语的性别中性翻译，通过提出专门的基准和探索自动评估方法来满足包容性语言的不断增长的需求。我们介绍了 GeNTE，这是一个自然、双语的性别中性翻译测试集，其创建受到有关中性语言感知和使用的调查的启发。基于 GeNTE，我们概述了现有的基于参考的评估方法，突出了它们的局限性，并提出了一种更适合评估性别中性翻译的无参考方法。

基于 GeNTE 语料库的性别中性机器翻译基准测试

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

Having recognized gender bias as a major issue affecting current translation
technologies, researchers have primarily attempted to mitigate it by working on
the data front. However, whether algorithmic aspects concur to exacerbate
unwanted outputs remains so far under-investigated. In this work, we bring the
analysis on gender bias in automatic translation onto a seemingly neutral yet
critical component: word segmentation. Can segmenting methods influence the
ability to translate gender? Do certain segmentation approaches penalize the
representation of feminine linguistic markings? We address these questions by
comparing 5 existing segmentation strategies on the target side of speech
translation systems. Our results on two language pairs (English-Italian/French)
show that state-of-the-art sub-word splitting (BPE) comes at the cost of higher
gender bias. In light of this finding, we propose a combined approach that
preserves BPE overall translation quality, while leveraging the higher ability
of character-based segmentation to properly translate gender.

本文探讨了中性的分词组件是否会影响到翻译性别，以及某些分词方法是否会惩罚女性语言标记的表示。作者比较了 5 种不同的分词策略，并发现最先进的子单词拆分技术 (BPE) 导致更高的性别偏见。因此，我们提出一种结合字符级分词更好地翻译性别，同时保留 BPE 整体翻译质量的方法。