
# Paper
This is an mBART-based model for responsibility perspective transfer, a novel text style transfer task of automatically rewriting gender-based violence descriptions as a means to alter the perceived level of responsibility on the perpetrator. It is introduced in the paper [Responsibility Perspective Transfer for Italian Femicide News](https://arxiv.org/abs/2306.00437v1).

# 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.

# How to use
```python
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

model = MBartForConditionalGeneration.from_pretrained("laihuiyuan/RPT")
tokenizer = MBart50TokenizerFast.from_pretrained("laihuiyuan/RPT", src_lang="it_IT")

source = "Provaglio d'Iseo , donna trovata morta in casa : si sospetta il compagno"
meta_info = "Simona Simonini, Elio Cadei, partner, percosse, Provaglio d'Iseo, c." #<victim name, perpetrator name, relationship, weapon, municipality, place>

inputs = meta_info + ' ' +source
inputs = tokenizer(inputs, return_tensors="pt")
decode_start_id =tokenizer.lang_code_to_id['it_IT']
output = model.generate(input_ids=inputs['input_ids'], num_beams=5, max_length=80, forced_bos_token_id=decode_start_id)
transferred_text = tokenizer.decode(output[0].tolist(), skip_special_tokens=True, clean_up_tokenization_spaces=False)
```

# Citation Info
```BibTeX
@inproceedings{minnemaa-etal-2023-responsibility,
    title = "Responsibility Perspective Transfer for Italian Femicide News",
    author = "Minnemaa, Gosse and Lai, Huiyuan and Muscato, Benedetta and Nissim, Malvina",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = July,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
}
```
{"language": ["en"], "license": "apache-2.0"}
transformers pytorch mbart text2text-generation en arxiv:2306.00437 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #en #arxiv-2306.00437 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# Paper
This is an mBART-based model for responsibility perspective transfer, a novel text style transfer task of automatically rewriting gender-based violence descriptions as a means to alter the perceived level of responsibility on the perpetrator. It is introduced in the paper Responsibility Perspective Transfer for Italian Femicide News.

# 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.

# How to use


Info

# Paper
This is an mBART-based model for responsibility perspective transfer, a novel text style transfer task of automatically rewriting gender-based violence descriptions as a means to alter the perceived level of responsibility on the perpetrator. It is introduced in the paper Responsibility Perspective Transfer for Italian Femicide News. # 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. # How to use


Info
TAGS
#transformers #pytorch #mbart #text2text-generation #en #arxiv-2306.00437 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Paper
This is an mBART-based model for responsibility perspective transfer, a novel text style transfer task of automatically rewriting gender-based violence descriptions as a means to alter the perceived level of responsibility on the perpetrator. It is introduced in the paper Responsibility Perspective Transfer for Italian Femicide News. # 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. # How to use


Info