Responsibility Perspective Transfer for I talian Femicide News (2023)

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.

## introduction if we want social stereotypes to be challenged, the language we use to describe gbv is thus an excellent place to start, also from a natural language processing (nlp) perspective. recent work has shown that perspectives on femicides and their triggered perceptions can be modelled automatically @xcite . in this paper, as shown in box ??, we explore the challenge of rewriting descriptions of gbv with the aim to increase the perceived level of blame on the perpetrator, casting it as a style transfer task @xcite . in this novel responsibility perspective transfer task, a given sentence from femicide news reports gets rewritten in a way that puts more responsibility on the perpetrator, while preserving the original content. contributions we create an evaluation set containing semi-aligned pairs with "low" and "high" sentences expressing similar information relative to an event, from an existing dataset of italian news ( §2.1). in absence of parallel training data, we follow previous work @xcite @xcite to train an unsupervised style transfer model using mbart @xcite on non-parallel data (with style labels) with a zero-shot and a few-shot approach using gpt-3 @xcite to perform rewriting ( §2.2). we run both human-based and automatic evaluations to assess the impact of rewriting on the perceived blame, comparing original and rephrased texts to find that models can achieve detectable perspective shifts ( §3). by introducing the novel task of responsibility perspective transfer, providing an evaluation dataset, a battery of trained models, and evidence of a successful methodology, we hope to foster further research and application developments on this and other perspective rewriting tasks that are relevant to society. foot_3 2 experimental settings ## discussion & conclusion we proposed responsibility perspective transfer as a new task and introduced a dataset and models for applying this task to italian news reporting about femicides. our dataset contains a limited amount of quasi-aligned pairs that proved useful for evaluation and few-shot learning. we experimented with two modeling approaches: unsupervised mbart (with or without enriching the input with metadata) and zero-shot/few-shot learning with gpt-3. our human and automatic evaluations suggest gpt-3/iter-1 as the best overall model, with a relatively high level of responsibility placed on the perpetrator and a good degree of content preservation. for the latter, most models score at least 6/10 on average on the human survey. the perspective change itself has also been achieved by our models, with substantially increased levels of perceived perpetrator blame compared to the source, but there is still much room for improvement: none of the models comes close to having the same level of blame as the target sentences do, and in the human eval-9 due to lack of space, we include only generations from the overall best-performing model from each category. prediction examples (ex. 1) provaglio d'iseo, donna trovata morta in casa : si sospetta il compagno "[town name], woman found dead at home: [her] partner is suspected" target: donna massacrata in casa nel bresciano, il convivente fermato per omicidio volontario "woman massacred at home in [area name]: [her] cohabitant [partner] arrested for voluntary homocide" mbart (meta-src): un uomo, elio cadei, è stato fermato per l'omicidio di simona simonini, la donna trovata morta in casa: si sospetta il compagno di lei. "a man, elio cadei, has been arrested for the murder of simona simonini, the woman found dead at home: the partner of her is suspected" gpt-3 (iter-1): provaglio d'iseo, donna trovata morta in casa: il compagno è il principale sospettato "[town name], woman found dead at home: [her] partner is the principal suspect" (ex. 2) «come vuole che ci si senta, quando ci sono stati due morti?» "«how do you want us to feel, when there have been two deaths?»" target: albenga, uccide l'ex compagna a coltellate e poi si toglie la vita loredana e l'incubo-stalker foto "[town name], [someone] kills [his] ex-partner by stabbing and then takes his life @xcite loredana and the nightmare stalker[, see] foto" mbart (meta-src): un uomo, mohamed aziz el mountassir, di 43 anni, ha ucciso la moglie, loredana colucci, di 44, con una coltellata al collo. "a man, mohamed aziz el mountassir, 43 years old, has killed his wife, loredana colucci, 44 years old, with a knife to [her] neck." gpt-3 (iter-1): «come vuole che ci si senta, quando ci sono stati due morti causati da un uomo che ha ucciso prima la sua ex moglie e poi la sua nuova compagna? "how do you want us to feel, when there have been two deaths caused by a man who has first killed his ex-wife and then his new partner?" box 1: prediction examples uation survey no model achieves a 'blame score' of more than 4.5/10. the main obstacle for future improvements seems to lie with the lack of truly parallel data; however, our gpt-3-based iterative approach of creating minimal pairs seems to have worked quite well, and might be further exploited on a larger scale.