Different ways of linguistically expressing the same real-world event can lead to different perceptions of what happened. Previous work has shown that different descriptions of gender-based violence (GBV) influence the reader's perception of who is to blame for the violence, possibly reinforcing stereotypes which see the victim as partly responsible, too. As a contribution to raise awareness on perspective-based writing, and to facilitate access to alternative perspectives, we introduce the novel task of automatically rewriting GBV descriptions as a means to alter the perceived level of responsibility on the perpetrator. We present a quasi-parallel dataset of sentences with low and high perceived responsibility levels for the perpetrator, and experiment with unsupervised (mBART-based), zero-shot and few-shot (GPT3-based) methods for rewriting sentences. We evaluate our models using a questionnaire study and a suite of automatic metrics.

翻译：同一现实事件的不同语言表达方式会导致人们对事件的不同认知。已有研究表明，针对性别暴力（GBV）的不同描述会影响读者对暴力责任方的判断，可能强化将受害者视为部分有责方的刻板印象。为提升对基于视角的写作方式的认知，并促进替代性视角的获取，我们提出了一项新任务：通过自动改写GBV描述来改变感知到的施暴者责任程度。我们构建了一个准平行数据集，其中包含对施暴者关联高低责任感知的句子，并实验了无监督（基于mBART）、零样本和少样本（基于GPT3）的句子改写方法。我们通过问卷研究和一组自动评估指标对模型进行了评价。