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

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