# Gender Bias Hidden Behind Chinese Word Embeddings: The Case of Chinese Adjectives

arXiv: https://arxiv.org/abs/2106.00181 · HTML: https://mlsanity.com/paper/2106.00181/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Learning Gender-Neutral Word Embeddings](https://mlsanity.com/paper/1809.01496/) (2018)
- [When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?](https://mlsanity.com/paper/1804.06323/) (2018)
- [Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate](https://mlsanity.com/paper/1809.02208/) (2018)
- [Analogical Reasoning on Chinese Morphological and Semantic Relations](https://mlsanity.com/paper/1805.06504/) (2018)
- [Unsupervised Discovery of Gendered Language through Latent-Variable Modeling](https://mlsanity.com/paper/1906.04760/) (2019)
- [Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990](https://mlsanity.com/paper/1907.08922/) (2019)

## Built on by (most cited first, 7 of 7)

- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation](https://mlsanity.com/paper/2301.00395/) (2023)
- [ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture](https://mlsanity.com/paper/2211.10780/) (2022)
- [Evaluating Gender Bias of Pre-trained Language Models in Natural Language Inference by Considering All Labels](https://mlsanity.com/paper/2309.09697/) (2023)
- [HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models](https://mlsanity.com/paper/2211.02882/) (2022)
- [Undesirable Biases in NLP: Addressing Challenges of Measurement](https://mlsanity.com/paper/2211.13709/) (2022)
- [Overview of the NLPCC 2025 Shared Task: Gender Bias Mitigation Challenge](https://mlsanity.com/paper/2506.12574/) (2025)

## Abstract (arXiv)

Gender bias in word embeddings gradually becomes a vivid research field in recent years. Most studies in this field aim at measurement and debiasing methods with English as the target language. This paper investigates gender bias in static word embeddings from a unique perspective, Chinese adjectives. By training word representations with different models, the gender bias behind the vectors of adjectives is assessed. Through a comparison between the produced results and a human-scored data set, we demonstrate how gender bias encoded in word embeddings differentiates from people's attitudes.