Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings

Jiali Li, Shucheng Zhu, Ying Liu, Pengyuan Liu


Abstract
Gender is a construction in line with social perception and judgment. An important means of this construction is through languages. When natural language processing tools, such as word embeddings, associate gender with the relevant categories of social perception and judgment, it is likely to cause bias and harm to those groups that do not conform to the mainstream social perception and judgment. Using 12,251 Chinese word embeddings as intermedium, this paper studies the relationship between social perception and judgment categories and gender. The results reveal that these grammatical gender-neutral Chinese word embeddings show a certain gender bias, which is consistent with the mainstream society’s perception and judgment of gender. Men are judged by their actions and perceived as bad, easily-disgusted, bad-tempered and rational roles while women are judged by their appearances and perceived as perfect, either happy or sad, and emotional roles.
Anthology ID:
2022.gebnlp-1.2
Volume:
Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
Month:
July
Year:
2022
Address:
Seattle, Washington
Venue:
GeBNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8–16
Language:
URL:
https://aclanthology.org/2022.gebnlp-1.2
DOI:
10.18653/v1/2022.gebnlp-1.2
Bibkey:
Cite (ACL):
Jiali Li, Shucheng Zhu, Ying Liu, and Pengyuan Liu. 2022. Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 8–16, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings (Li et al., GeBNLP 2022)
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PDF:
https://aclanthology.org/2022.gebnlp-1.2.pdf
Video:
 https://aclanthology.org/2022.gebnlp-1.2.mp4