# Unsupervised Discovery of Gendered Language through Latent-Variable Modeling

arXiv: https://arxiv.org/abs/1906.04760 · HTML: https://mlsanity.com/paper/1906.04760/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 19 of 19)

- [A Survey on Gender Bias in Natural Language Processing](https://mlsanity.com/paper/2112.14168/) (2021)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Investigating Failures of Automatic Translation in the Case of Unambiguous Gender](https://mlsanity.com/paper/2104.07838/) (2021)
- [Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models](https://mlsanity.com/paper/2104.07505/) (2021)
- [Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)](https://mlsanity.com/paper/2410.19314/) (2024)
- [Discovering Differences in the Representation of People using Contextualized Semantic Axes](https://mlsanity.com/paper/2210.12170/) (2022)
- [HumT DumT: Measuring and controlling human-like language in LLMs](https://mlsanity.com/paper/2502.13259/) (2025)
- [Gender Bias Hidden Behind Chinese Word Embeddings: The Case of Chinese Adjectives](https://mlsanity.com/paper/2106.00181/) (2021)
- [Quantifying Gender Biases Towards Politicians on Reddit](https://mlsanity.com/paper/2112.12014/) (2021)
- [Measuring Intersectional Biases in Historical Documents](https://mlsanity.com/paper/2305.12376/) (2023)
- [Second Order WinoBias (SoWinoBias) Test Set for Latent Gender Bias Detection in Coreference Resolution](https://mlsanity.com/paper/2109.14047/) (2021)
- [Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings](https://mlsanity.com/paper/2307.10200/) (2023)
- [The Causal Influence of Grammatical Gender on Distributional Semantics](https://mlsanity.com/paper/2311.18567/) (2023)
- [Are Female Carpenters like Blue Bananas? A Corpus Investigation of Occupation Gender Typicality](https://mlsanity.com/paper/2408.02948/) (2024)
- [Algorithmic Fairness: A Tolerance Perspective](https://mlsanity.com/paper/2405.09543/) (2024)
- [Interactive Topic Models with Optimal Transport](https://mlsanity.com/paper/2406.19928/) (2024)
- [Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures](https://mlsanity.com/paper/2603.17952/) (2026)
- [Personality Shapes Gender Bias in Persona-Conditioned LLM Narratives Across English and Hindi: An Empirical Investigation](https://mlsanity.com/paper/2604.23600/) (2026)
- [Speak Your Mind: The Speech Continuation Task as a Probe of Voice-Based Model Bias](https://mlsanity.com/paper/2509.22061/) (2025)

## Abstract (arXiv)

Studying the ways in which language is gendered has long been an area of interest in sociolinguistics. Studies have explored, for example, the speech of male and female characters in film and the language used to describe male and female politicians. In this paper, we aim not to merely study this phenomenon qualitatively, but instead to quantify the degree to which the language used to describe men and women is different and, moreover, different in a positive or negative way. To that end, we introduce a generative latent-variable model that jointly represents adjective (or verb) choice, with its sentiment, given the natural gender of a head (or dependent) noun. We find that there are significant differences between descriptions of male and female nouns and that these differences align with common gender stereotypes: Positive adjectives used to describe women are more often related to their bodies than adjectives used to describe men.