# Examining Gender Bias in Languages with Grammatical Gender

arXiv: https://arxiv.org/abs/1909.02224 · HTML: https://mlsanity.com/paper/1909.02224/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 30 of 40)

- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models](https://mlsanity.com/paper/2110.08527/) (2021)
- [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)
- [Gender Bias in Masked Language Models for Multiple Languages](https://mlsanity.com/paper/2205.00551/) (2022)
- [Comparing Biases and the Impact of Multilingual Training across Multiple Languages](https://mlsanity.com/paper/2305.11242/) (2023)
- [Gender Bias in Large Language Models across Multiple Languages](https://mlsanity.com/paper/2403.00277/) (2024)
- [Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources](https://mlsanity.com/paper/2103.11320/) (2021)
- [Socially Aware Bias Measurements for Hindi Language Representations](https://mlsanity.com/paper/2110.07871/) (2021)
- [Sociodemographic Bias in Language Models: A Survey and Forward Path](https://mlsanity.com/paper/2306.08158/) (2023)
- [Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation](https://mlsanity.com/paper/2203.09866/) (2022)
- [Evaluating Gender Bias in Hindi-English Machine Translation](https://mlsanity.com/paper/2106.08680/) (2021)
- [CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation](https://mlsanity.com/paper/2301.00395/) (2023)
- [Fairness in Language Models Beyond English: Gaps and Challenges](https://mlsanity.com/paper/2302.12578/) (2023)
- [Assessing the Reliability of Word Embedding Gender Bias Measures](https://mlsanity.com/paper/2109.04732/) (2021)
- [Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic](https://mlsanity.com/paper/2204.03558/) (2022)
- [Do Multilingual Large Language Models Mitigate Stereotype Bias?](https://mlsanity.com/paper/2407.05740/) (2024)
- [Analyzing Gender Representation in Multilingual Models](https://mlsanity.com/paper/2204.09168/) (2022)
- [WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings](https://mlsanity.com/paper/2103.03598/) (2021)
- [Debiasing Multilingual Word Embeddings: A Case Study of Three Indian Languages](https://mlsanity.com/paper/2107.10181/) (2021)
- [HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models](https://mlsanity.com/paper/2211.02882/) (2022)
- [Evaluating Metrics for Bias in Word Embeddings](https://mlsanity.com/paper/2111.07864/) (2021)
- [Towards Robust NLG Bias Evaluation with Syntactically-diverse Prompts](https://mlsanity.com/paper/2212.01700/) (2022)
- [Mitigating Gender Stereotypes in Hindi and Marathi](https://mlsanity.com/paper/2205.05901/) (2022)
- [SWEAT: Scoring Polarization of Topics across Different Corpora](https://mlsanity.com/paper/2109.07231/) (2021)
- [Measuring Gender Bias in Word Embeddings of Gendered Languages Requires Disentangling Grammatical Gender Signals](https://mlsanity.com/paper/2206.01691/) (2022)
- [Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Terms](https://mlsanity.com/paper/2503.04372/) (2025)
- [Undesirable Biases in NLP: Addressing Challenges of Measurement](https://mlsanity.com/paper/2211.13709/) (2022)
- [Efficient Gender Debiasing of Pre-trained Indic Language Models](https://mlsanity.com/paper/2209.03661/) (2022)
- [A methodology to characterize bias and harmful stereotypes in natural language processing in Latin America](https://mlsanity.com/paper/2207.06591/) (2022)

## Abstract (arXiv)

Recent studies have shown that word embeddings exhibit gender bias inherited from the training corpora. However, most studies to date have focused on quantifying and mitigating such bias only in English. These analyses cannot be directly extended to languages that exhibit morphological agreement on gender, such as Spanish and French. In this paper, we propose new metrics for evaluating gender bias in word embeddings of these languages and further demonstrate evidence of gender bias in bilingual embeddings which align these languages with English. Finally, we extend an existing approach to mitigate gender bias in word embeddings under both monolingual and bilingual settings. Experiments on modified Word Embedding Association Test, word similarity, word translation, and word pair translation tasks show that the proposed approaches effectively reduce the gender bias while preserving the utility of the embeddings.