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        <title>Responsibility Perspective Transfer for Italian Femicide News</title>
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    <name type="personal">
        <namePart type="given">Malvina</namePart>
        <namePart type="family">Nissim</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-acl.501</identifier>
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%0 Conference Proceedings
%T Responsibility Perspective Transfer for Italian Femicide News
%A Minnema, Gosse
%A Lai, Huiyuan
%A Muscato, Benedetta
%A Nissim, Malvina
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F minnema-etal-2023-responsibility
%X 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.
%R 10.18653/v1/2023.findings-acl.501
%U https://aclanthology.org/2023.findings-acl.501
%U https://doi.org/10.18653/v1/2023.findings-acl.501
%P 7907-7918

Markdown (Informal)

[Responsibility Perspective Transfer for Italian Femicide News](https://aclanthology.org/2023.findings-acl.501) (Minnema et al., Findings 2023)

• Responsibility Perspective Transfer for Italian Femicide News (Minnema et al., Findings 2023)

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.