Responsibility Perspective Transfer for Italian Femicide News

Gosse Minnema, Huiyuan Lai, Benedetta Muscato, Malvina Nissim

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
57 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics, ACL 2023
EditorsAnna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
PublisherAssociation for Computational Linguistics, ACL Anthology
Pages7907-7918
Number of pages12
ISBN (Electronic)9781959429623
DOIs
Publication statusPublished - 2023
Event61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada
Duration: 9-Jul-202314-Jul-2023

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
Country/TerritoryCanada
CityToronto
Period09/07/202314/07/2023

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