Reflecting the Male Gaze: Quantifying Female Objectification in 19th and 20th Century Novels (2024.lrec-main)
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| Challenge: | a framework for analyzing gender bias in terms of female objectification is proposed . male gaze refers to a phenomenon in which women are depicted as objects of aesthetic pleasure . |
| Approach: | They propose a framework for analyzing gender bias in terms of female objectification . they compute an agency bias score that indicates whether male entities are more likely to appear in the text as grammatical agents than female entities . |
| Outcome: | The proposed framework measures female objectification along two axes. |
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| Challenge: | Recent approaches to visual understanding of image captioning rely on transformers and pre-trained paradigms to learn cross-modal representation. |
| Approach: | They propose a visual semantic-based gender score that measures the degree of bias and can be used as a plug-in for any image captioning system. |
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Gender Stereotypes Differ between Male and Female Writings (P19-2)
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| Challenge: | a new study quantitatively evaluates gender stereotypes in written language . female writings contain fewer gender stereotype scores than male writings . |
| Approach: | They quantitatively evaluate and analyze gender stereotypes in written language . they compare writings by female authors with writings from male authors . |
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Tales and Tropes: Gender Roles from Word Embeddings in a Century of Children’s Books (2022.coling-1)
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Anjali Adukia, Patricia Chiril, Callista Christ, Anjali Das, Alex Eble, Emileigh Harrison, Hakizumwami Birali Runesha
| Challenge: | In 100 years of influential children's books, gender is portrayed in a way that reproduces traditional gender norms in society. |
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Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs (2020.aacl-main)
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| Challenge: | Existing methods to quantify gender bias in word embeddings are not robust and cannot identify common types of bias. |
| Approach: | They propose to quantify gender bias by using cosine similarity to a pair of gender words and using analogies. |
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Revisiting the Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and Poems (2024.lrec-main)
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| Challenge: | This study highlights the pervasive existence of gender stereotypes in literary works and proposes a model with 97% accuracy to identify gender bias. |
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Examining Gender Bias in Languages with Grammatical Gender (D19-1)
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| Challenge: | Existing studies on gender bias in word embeddings focus on English . however, these studies cannot be extended to languages with morphological agreement on gender . |
| Approach: | They propose new metrics to evaluate gender bias in word embeddings of English and Spanish . they extend existing approaches to mitigate gender bias while preserving original embeddables . |
| Outcome: | The proposed methods reduce gender bias while preserving the original embeddings. |
Gender Bias in Contextualized Word Embeddings (N19-1)
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| Challenge: | Existing studies show that training word embeddings in large corpora could lead to encoding societal biases present in these human-produced data. |
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“Fifty Shades of Bias”: Normative Ratings of Gender Bias in GPT Generated English Text (2023.emnlp-main)
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| Challenge: | Prior work treats gender bias as a binary classification task, but a comparative annotation framework can be used to assess the impact of biases. |
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Uncovering Implicit Gender Bias in Narratives through Commonsense Inference (2021.findings-emnlp)
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| Challenge: | Pre-trained language models learn harmful biases from their training corpora and may repeat these biase if used for generation. |
| Approach: | They focus on gender biases associated with the protagonist in model-generated stories and use a commonsense reasoning engine to uncover them. |
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Identifying and Reducing Gender Bias in Word-Level Language Models (N19-3)
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| Challenge: | Existing discriminatory biases in training data can be amplified by models . text corpora exhibit socially problematic biase . |
| Approach: | They propose a metric to measure gender bias and a regularization loss term to minimize embeddings onto an embeddable subspace that encodes gender. |
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