Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (P19-1)
Copied to clipboard
| 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. |
Similar Papers
Different Speech Translation Models Encode and Translate Speaker Gender Differently (2025.acl-short)
Copied to clipboard
| 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. |
Leveraging Pre-trained Language Models for Gender Debiasing (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing methods to reduce gender bias in natural language are costly and time-consuming. |
| Approach: | They propose a method to generate gender variants for a given text using pre-trained language models as the resource without any task-specific labelled data. |
| Outcome: | The proposed method can reduce gender bias in a language generation context without a task-specific labelled data. |
Automatically Inferring Gender Associations from Language (D19-1)
Copied to clipboard
| 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. |
RtGender: A Corpus for Studying Differential Responses to Gender (L18-1)
Copied to clipboard
| 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). |
Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation (2022.acl-long)
Copied to clipboard
| Challenge: | grammatical gender languages are characterized by morphosyntactic chains of gender agreement marked on a variety of lexical items and parts-of-speech (POS). |
| Approach: | They propose to enrich the natural, gender-sensitive MuST-SHE corpus with two new linguistic annotation layers to explore gender bias. |
| Outcome: | The proposed models shed light on gender bias and its detection at several levels of granularity. |
“Feels Feminine to Me”: Understanding Perceived Gendered Style through Human Annotations (2025.emnlp-main)
Copied to clipboard
| Challenge: | Using gender identity-based framing, language–gender associations are often grounded in the author’s gender identity, inferred from their language use. |
| Approach: | They propose to operationalize the language–gender association as a perceived gender expression of language, focusing on how expression is externally interpreted by humans, independent of the author’s gender identity. |
| Outcome: | The first dataset of itskind identifies 5,100 human annotations of perceived gendered style—human-written texts rated on a five-point scale from very feminine to very masculine. |
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs (2021.tacl-1)
Copied to clipboard
| Challenge: | In many languages, nouns possess grammatical genders. |
| Approach: | They use large-scale corpora and tools from NLP and information theory to test whether there is a relationship between grammatical genders of inanimate nouns and adjectives used to describe them. |
| Outcome: | The results show that there is a statistically significant relationship between the grammatical genders of inanimate nouns and adjectives used to describe them in all six languages. |
What social attitudes about gender does BERT encode? Leveraging insights from psycholinguistics (2023.acl-long)
Copied to clipboard
| 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. |
MISGENDERED: Limits of Large Language Models in Understanding Pronouns (2023.acl-long)
Copied to clipboard
| Challenge: | excluding non-binary gender identities can perpetuate harm against non-bisexual individuals through exclusion and marginalization. |
| Approach: | They propose a framework for evaluating large language models’ ability to correctly use preferred pronouns. |
| Outcome: | The proposed framework evaluates language models' ability to correctly use preferred pronouns in English. |
Quantifying the Semantic Core of Gender Systems (D19-1)
Copied to clipboard
| Challenge: | a large number of languages employ grammatical gender on the lexeme, but is it truly arbitrary? a recent study shows that the relationship between grammamatical gender and lexical semantics is opaque. |
| Approach: | They propose a method to correlating inanimate nouns' gender with lexical semantics . they find that the gender systems of 18 languages exhibit a significant correlation with a definition . |
| Outcome: | a new study shows that the gender assignments of 18 languages are arbitrary . the authors show that the correlation between gender and semantics is significant . |