Create README.md

README.md

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+---
+language:
+- en
+license: apache-2.0
+---
+
+# Paper
+This is an mBART-based model for responsibility perspective transfer, a novel text style transfer task of automatically rewriting GBV 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 + ' ' +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",
+}
+```