  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.
