TY - GEN
T1 - Responsibility Perspective Transfer for Italian Femicide News
AU - Minnema, Gosse
AU - Lai, Huiyuan
AU - Muscato, Benedetta
AU - Nissim, Malvina
N1 - Publisher Copyright:
© 2023 Association for Computational Linguistics.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85175479458&partnerID=8YFLogxK
U2 - 10.18653/v1/2023.findings-acl.501
DO - 10.18653/v1/2023.findings-acl.501
M3 - Conference contribution
AN - SCOPUS:85175479458
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 7907
EP - 7918
BT - Findings of the Association for Computational Linguistics, ACL 2023
A2 - Rogers, Anna
A2 - Boyd-Graber, Jordan
A2 - Okazaki, Naoaki
PB - Association for Computational Linguistics, ACL Anthology
T2 - 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
Y2 - 9 July 2023 through 14 July 2023
ER -