Misgendering is the act of referring to someone in a way that does not reflect their gender identity. Translation systems, including foundation models capable of translation, can produce errors that result in misgendering harms. To measure the extent of such potential harms when translating into and out of English, we introduce a dataset, MiTTenS, covering 26 languages from a variety of language families and scripts, including several traditionally underpresented in digital resources. The dataset is constructed with handcrafted passages that target known failure patterns, longer synthetically generated passages, and natural passages sourced from multiple domains. We demonstrate the usefulness of the dataset by evaluating both dedicated neural machine translation systems and foundation models, and show that all systems exhibit errors resulting in misgendering harms, even in high resource languages.

翻译：误称性别是指以不符合个人性别认同的方式称呼他人的行为。翻译系统（包括具备翻译能力的基础模型）可能产生导致误称性别伤害的错误。为衡量英译及译英过程中此类潜在危害的程度，我们推出了MiTTenS数据集，覆盖26种语言，这些语言来自不同语系及书写系统，其中包含若干传统数字资源中代表性不足的语言。该数据集由三类语料构成：针对已知失败模式手工编写的段落、较长的合成生成段落，以及来自多个领域的自然段落。我们通过评估专用神经机器翻译系统和基础模型，验证了该数据集的实用性，结果显示所有系统均存在导致误称性别伤害的错误，即便在高资源语言中也不例外。