Gender-neutral translation (GNT) that avoids biased and undue binary
assumptions is a pivotal challenge for the creation of more inclusive
translation technologies. Advancements for this task in Machine Translation
(MT), however, are hindered by the lack of dedicated parallel data, which are
necessary to adapt MT systems to satisfy neutral constraints. For such a
scenario, large language models offer hitherto unforeseen possibilities, as
they come with the distinct advantage of being versatile in various (sub)tasks
when provided with explicit instructions. In this paper, we explore this
potential to automate GNT by comparing MT with the popular GPT-4 model. Through
extensive manual analyses, our study empirically reveals the inherent
limitations of current MT systems in generating GNTs and provides valuable
insights into the potential and challenges associated with prompting for
neutrality.

通过与 GPT-4 模型比较，我们通过广泛的手动分析实证揭示了当前机器翻译系统在生成性别中立翻译时的固有限制，并为提示中立性所涉及的潜力和挑战提供了有价值的见解。

对自动性别中性翻译需求的迅速响应

A Prompt Response to the Demand for Automatic Gender-Neutral Translation

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

Gender inclusivity in language has become a central topic of debate and
research. Its application in the cross-lingual contexts of human and machine
translation (MT), however, remains largely unexplored. Here, we discuss
Gender-Neutral Translation (GNT) as a form of gender inclusivity in translation
and advocate for its adoption for MT models, which have been found to
perpetuate gender bias and discrimination. To this aim, we review a selection
of relevant institutional guidelines for Gender-Inclusive Language (GIL) to
collect and systematize useful strategies of gender neutralization. Then, we
discuss GNT and its scenarios of use, devising a list of desiderata. Finally,
we identify the main technical challenges to the implementation of GNT in MT.
Throughout these contributions we focus on translation from English into
Italian, as representative of salient linguistic transfer problems, due to the
different rules for gender marking in their grammar.

本文讨论性别中立翻译在机器翻译中的应用，并倡导其采用，以解决机器翻译模型延续性别偏见和歧视的问题。作者回顾了有关性别中立语言的相关机构指南，提出了性别中立翻译的策略和使用场景，并探讨了机器翻译中实施性别中立翻译的主要技术挑战。全文重点讨论了英语到意大利语的翻译问题，以代表性别标记规则不同的语言转换难题。