| Challenge: | In this paper, we demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. |
| Approach: | They propose to integrate two datasets and a novel approach to automatically infer gender associations from language and find coherent word clusters and label clusters for the semantic concepts they represent. |
| Outcome: | The proposed methods outperform strong baselines in large-scale studies of how people talk about women and men in two different settings. |
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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 . |
| Outcome: | The results show that writings by female authors have lower gender stereotype scores . the authors plan on using more datasets over the past century to study gender stereotypes . |
Exploring Human Gender Stereotypes with Word Association Test (D19-1)
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| Challenge: | Existing word embeddings have been used to study gender stereotypes in texts . however, evaluating their validities is still an open problem . et al.: this study investigates gender bias using the lens of language, especially, the words . |
| Approach: | They use word association test to derive bias scores for large amount of words . they find that these bias scores correlate well with bias in the real world . |
| Outcome: | The proposed method correlates well with bias in the real world, and with census data, it provides a different perspective on gender stereotypes in words. |
Blind Men and the Elephant: Diverse Perspectives on Gender Stereotypes in Benchmark Datasets (2025.emnlp-main)
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| Challenge: | Existing benchmarks for measuring gender stereotypical bias in language models are inconsistencies . lack of explicit standards in data gathering can have detrimental effects on results . |
| Approach: | They propose that currently available benchmarks capture only partial facets of gender stereotypes . they apply a framework from social psychology to balance data across components of gender stereotypes based on stereotypical benchmarks. |
| Outcome: | The proposed framework improves correlation between different benchmarks by using simple balancing techniques. |
Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (P19-1)
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| Challenge: | a recent study has focused on the ways in which language is gendered . positive adjectives used to describe women are more often related to their bodies . |
| Approach: | They propose a model that models adjective choice and its sentiment given the natural gender of a head noun. |
| Outcome: | The proposed model shows that positive adjectives used to describe women are more often related to their bodies than positive adjective words used to explain men. |
What social attitudes about gender does BERT encode? Leveraging insights from psycholinguistics (2023.acl-long)
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| Challenge: | Much research has focused on evaluating whether large language models encode stereotypical/harmful associations. |
| Approach: | They propose to use two datasets from human experiments to examine how word preferences in a large language model reflect social attitudes about gender. |
| Outcome: | The language model BERT takes into account factors that shape human lexical choice of such language, but may not weigh those factors in the same way people do. |
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. |
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. |
| Outcome: | The proposed method reduces gender bias up to an optimal weight assigned to the loss term, and the model becomes unstable as the perplexity increases. |
RtGender: A Corpus for Studying Differential Responses to Gender (L18-1)
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| Challenge: | Prior work on linguistic gender difference and communications about gender has focused on language about or portraying persons of a particular gender. |
| Approach: | They present a multi-genre corpus of 25M comments from five socially and topically diverse sources tagged for the gender of the addressee and 30k annotations for sentiment and relevance of these responses. |
| Outcome: | The proposed dataset shows that responses to women are more emotive and about the speaker as an individual (rather than about the content being responded to). |
Different Speech Translation Models Encode and Translate Speaker Gender Differently (2025.acl-short)
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| Challenge: | Recent studies on interpreting the hidden states of speech models have shown their ability to capture speaker-specific features, including gender. |
| Approach: | They propose to use probing methods to assess gender encoding across ST models. |
| Outcome: | The proposed models capture speaker-specific features, including gender, while older models do not . low gender encoding capabilities result in systems’ tendency toward a masculine default, a translation bias that is more pronounced in newer architectures. |
Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing (P19-1)
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| Challenge: | linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have not adapted to account for these differences. |
| Approach: | They annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles’ authors and build taggers and parsers trained on this data. |
| Outcome: | The proposed model can account for gendered differences in syntactic tasks and highlight future venues for developing more accurate taggers and parsers. |