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--- |
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language: |
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- en |
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license: apache-2.0 |
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--- |
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# Paper |
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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). |
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# Abstract |
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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. |
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# How to use |
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```python |
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from transformers import MBartForConditionalGeneration, MBart50TokenizerFast |
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model = MBartForConditionalGeneration.from_pretrained("laihuiyuan/RPT") |
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tokenizer = MBart50TokenizerFast.from_pretrained("laihuiyuan/RPT", src_lang="it_IT") |
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source = "Provaglio d'Iseo , donna trovata morta in casa : si sospetta il compagno" |
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meta_info = "Simona Simonini, Elio Cadei, partner, percosse, Provaglio d'Iseo, c." #<victim name, perpetrator name, relationship, weapon, municipality, place> |
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inputs = meta_info + ' ' +source |
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inputs = tokenizer(inputs, return_tensors="pt") |
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decode_start_id =tokenizer.lang_code_to_id['it_IT'] |
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output = model.generate(input_ids=inputs['input_ids'], num_beams=5, max_length=80, forced_bos_token_id=decode_start_id) |
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transferred_text = tokenizer.decode(output[0].tolist(), skip_special_tokens=True, clean_up_tokenization_spaces=False) |
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``` |
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# Citation Info |
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```BibTeX |
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@inproceedings{minnemaa-etal-2023-responsibility, |
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title = "Responsibility Perspective Transfer for Italian Femicide News", |
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author = "Minnemaa, Gosse and Lai, Huiyuan and Muscato, Benedetta and Nissim, Malvina", |
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booktitle = "Findings of the Association for Computational Linguistics: ACL 2023", |
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month = July, |
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year = "2023", |
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address = "Toronto, Canada", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |
