Responsibility Perspective Transfer for Italian Femicide News

Gosse Minnema, Huiyuan Lai, Benedetta Muscato, Malvina Nissim


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 blame 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.
Anthology ID:
2023.findings-acl.501
Original:
2023.findings-acl.501v1
Version 2:
2023.findings-acl.501v2
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7907–7918
Language:
URL:
https://aclanthology.org/2023.findings-acl.501
DOI:
10.18653/v1/2023.findings-acl.501
Bibkey:
Cite (ACL):
Gosse Minnema, Huiyuan Lai, Benedetta Muscato, and Malvina Nissim. 2023. Responsibility Perspective Transfer for Italian Femicide News. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7907–7918, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Responsibility Perspective Transfer for Italian Femicide News (Minnema et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-acl.501.pdf
Video:
 https://aclanthology.org/2023.findings-acl.501.mp4