Responsibility Perspective Transfer for Italian Femicide News
This work addresses the issue of biased language in news reporting for Italian femicide, aiming to raise awareness and provide alternative perspectives, though it is incremental as it builds on prior linguistic analysis.
The paper tackles the problem of altering perceived perpetrator responsibility in gender-based violence news by introducing an automatic rewriting task, achieving results through unsupervised and few-shot methods evaluated with human studies and automatic metrics.
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.