@inproceedings{jiao-luo-2021-gender,
    title = "Gender Bias Hidden Behind {C}hinese Word Embeddings: The Case of {C}hinese Adjectives",
    author = "Jiao, Meichun  and
      Luo, Ziyang",
    editor = "Costa-jussa, Marta  and
      Gonen, Hila  and
      Hardmeier, Christian  and
      Webster, Kellie",
    booktitle = "Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.gebnlp-1.2",
    doi = "10.18653/v1/2021.gebnlp-1.2",
    pages = "8--15",
    abstract = "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.",
}