Recent years have seen an increasing need for gender-neutral and inclusive
language. Within the field of NLP, there are various mono- and bilingual use
cases where gender inclusive language is appropriate, if not preferred due to
ambiguity or uncertainty in terms of the gender of referents. In this work, we
present a rule-based and a neural approach to gender-neutral rewriting for
English along with manually curated synthetic data (WinoBias+) and natural data
(OpenSubtitles and Reddit) benchmarks. A detailed manual and automatic
evaluation highlights how our NeuTral Rewriter, trained on data generated by
the rule-based approach, obtains word error rates (WER) below 0.18% on
synthetic, in-domain and out-domain test sets.

提出了一种针对英语的基于规则和神经网络的性别中立改写方法，使用手工和自动生成数据进行了评估。实验结果表明，我们的 NeuTral Rewriter 针对合成、领域内和领域外测试结果的字错误率（WER）低于 0.18％。

NeuTral Rewriter：基于规则和神经网络的性别中性化自动改写方法

NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic  Rewriting into Gender-Neutral Alternatives

As Machine Translation (MT) has become increasingly more powerful,
accessible, and widespread, the potential for the perpetuation of bias has
grown alongside its advances. While overt indicators of bias have been studied
in machine translation, we argue that covert biases expose a problem that is
further entrenched. Through the use of the gender-neutral language Turkish and
the gendered language English, we examine cases of both overt and covert gender
bias in MT models. Specifically, we introduce a method to investigate
asymmetrical gender markings. We also assess bias in the attribution of
personhood and examine occupational and personality stereotypes through overt
bias indicators in MT models. Our work explores a deeper layer of bias in MT
models and demonstrates the continued need for language-specific,
interdisciplinary methodology in MT model development.

本文研究了机器翻译 (MT) 中的偏见问题。通过使用性别中立的土耳其语和性别化的英语，本文探讨了 MT 模型中明显和潜在的性别偏见现象。研究者提出了一种方法来研究不对称性别标记，并通过评估人格化职业和人格刻板印象的表达方式揭示了 MT 模型中的偏见问题。本文强调了 MT 模型开发需要更多具有跨学科背景的语言详细分析的研究。