mlsanityData to 14 Oct 2026

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

arXiv 2106.00181 ↗First posted 1 Jun 2021cs.CLcited by 7

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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.

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

14 of 26 references.

References · 14

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  3. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  4. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  5. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  6. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  7. 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
  8. 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 253
  9. 2018Learning Gender-Neutral Word Embeddingscited by 169
  10. 2018When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?cited by 126
  11. 2018Assessing Gender Bias in Machine Translation -- A Case Study with Google Translatecited by 121
  12. 2018Analogical Reasoning on Chinese Morphological and Semantic Relationscited by 50
2 more
  1. 2019Unsupervised Discovery of Gendered Language through Latent-Variable Modelingcited by 19
  2. 2019Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990cited by 11

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Cited by year

Citing papers per year: 2022: 4; 2023: 2; 2024: 0; 2025: 1.

7 papers.

  1. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  2. 2023CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigationcited by 19
  3. 2022ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culturecited by 17
  4. 2023Evaluating Gender Bias of Pre-trained Language Models in Natural Language Inference by Considering All Labelscited by 16
  5. 2022HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Modelscited by 8
  6. 2022Undesirable Biases in NLP: Addressing Challenges of Measurementcited by 4
  7. 2025Overview of the NLPCC 2025 Shared Task: Gender Bias Mitigation Challengecited by 0