Content Warning: This paper contains examples of misgendering and erasure
that could be offensive and potentially triggering.
Misgendering, the act of incorrectly addressing someone's gender, inflicts
serious harm and is pervasive in everyday technologies, yet there is a notable
lack of research to combat it. We are the first to address this lack of
research into interventions for misgendering by conducting a survey of
gender-diverse individuals in the US to understand perspectives about automated
interventions for text-based misgendering. Based on survey insights on the
prevalence of misgendering, desired solutions, and associated concerns, we
introduce a misgendering interventions task and evaluation dataset,
MisgenderMender. We define the task with two sub-tasks: (i) detecting
misgendering, followed by (ii) correcting misgendering where misgendering is
present in domains where editing is appropriate. MisgenderMender comprises 3790
instances of social media content and LLM-generations about non-cisgender
public figures, annotated for the presence of misgendering, with additional
annotations for correcting misgendering in LLM-generated text. Using this
dataset, we set initial benchmarks by evaluating existing NLP systems and
highlighting challenges for future models to address. We release the full
dataset, code, and demo at
this https URL

通过对美国的性别多样化个体进行调查，本文是第一篇针对误用称谓所缺乏的干预研究进行调查，并根据调查结果提出了误用称谓干预任务和评估数据集 MisgenderMender，该数据集包括社交媒体内容和关于非二元性别公众人物的文本，通过检测和纠正误用称谓的存在，评估目前的自然语言处理系统并提出未来模型需解决的挑战。