Responsibility Perspective Transfer for Italian Femicide News (2023.findings-acl)

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Challenge: Existing work has shown that different descriptions of gender-based violence influence the reader’s perception of who is to blame for the violence.
Approach: They propose to automatically rewrite GBV descriptions to alter the perceived level of blame on the perpetrator.
Outcome: The proposed task alters perceived responsibility levels for perpetrators by using unsupervised, zero-shot and few-shot methods.

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Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports (2022.aacl-main)

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Challenge: linguistic expressions of gender-based violence can conceptualize the same event from different perspectives by emphasizing certain participants over others.
Approach: They conduct a large-scale perception survey of GBV descriptions from italian newspapers and train regression models that predict the salience of GV participants with respect to different dimensions of perceived responsibility.
Outcome: The proposed model shows that salient focus is more predictable than salient blame, and perpetrators’ salience is more predictable than victims’ salient.
Perspective Taking through Generating Responses to Conflict Situations (2024.findings-acl)

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Challenge: Language models struggle to understand and explain the beliefs of others, despite improving performance on a wide variety of tasks.
Approach: They propose to modify the social-chem-101 corpus to allow for perspective-taking, the process of conceptualizing the point of view of another person.
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Re-examining Sexism and Misogyny Classification with Annotator Attitudes (2024.findings-emnlp)

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Challenge: Existing datasets for content moderation fail to capture plurality of possible annotator perspectives or ensure representation of affected groups.
Approach: They examine the relationship between annotator identities and attitudes and the responses they give to two GBV labelling tasks.
Outcome: The results show that higher Right Wing Authoritarianism scores are associated with a higher propensity to label text as sexist . higher scores are also associated with negative attitudes towards sexism and neosexist attitudes .
Don’t Take This Out of Context!: On the Need for Contextual Models and Evaluations for Stylistic Rewriting (2023.emnlp-main)

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Challenge: Existing stylistic text rewriting methods ignore the context of the text, causing generic, incoherent, and generic outputs.
Approach: They propose a contextual evaluation metric that integrates preceding context into stylistic text rewriting.
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#NotAWhore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media (2020.acl-srw)

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Challenge: Recent surge in online forums and movements supporting sexual assault survivors has led to the emergence of a ‘virtual bubble’ where survivors can recount their stories.
Approach: They propose a transfer-learning based method to identify victim blaming language on Twitter and a single step transfer-based classification method to classify it.
Outcome: The proposed method is compared with various deep learning and machine learning models on a manually annotated domain-specific dataset.
Entity Framing and Role Portrayal in the News (2025.findings-acl)

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Challenge: a dataset of news articles containing 22 fine-grained characters is annotated for entity framing and role portrayal . the dataset includes 1,378 recent news articles in five languages focusing on the Ukraine-Russia War and climate change .
Approach: They propose a multilingual and hierarchical corpus annotated for entity framing and role portrayal in news articles.
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PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction (2020.emnlp-main)

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Challenge: Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction.
Approach: They propose a new revision task that debiases text through the lens of connotation frames to correct implicit biases in character portrayals.
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Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation (2021.findings-emnlp)

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Challenge: Recent studies have found evidence of gender bias in machine translation and coreference resolution models using mostly synthetic diagnostic datasets.
Approach: They propose a semi-automatic method to vastly extend synthetic, small diagnostic datasets to include grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments.
Outcome: The proposed method extends the existing dataset to 108K diverse English sentences.
He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues (2022.findings-emnlp)

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Challenge: Existing work on style transfer has focused on controlling formality, authorial style, and sentiment of text.
Approach: They propose a style transfer task that reframes a dialogue from informal first person to formal third person rephrasing . they use a dataset to annotate dialogues from a text summarization corpus .
Outcome: The proposed task improves the performance of extractive models on a dialogue summarization dataset.
GeNRe: A French Gender-Neutral Rewriting System Using Collective Nouns (2025.findings-acl)

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Challenge: Gender rewriting is an NLP task that uses gendered forms to mitigate gender biases.
Approach: They propose a French gender-neutral rewriting system using collective nouns, which are gender-fixed in French.
Outcome: The proposed system detects gendered forms and replaces them with neutral or opposite forms.

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