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

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.BIAS STATEMENT This paper studies gender bias in Chinese adjectives, captured by word embeddings. For each Chinese adjective, a gender bias score is calculated by w • ( he − she) (Bolukbasi et al., 2016). A positive score represents the Chinese adjective word embeddings is more associated with males, and a negative value refers to the opposite result. In our daily life, we find that gender stereotypes can be conveyed by adjectives. The close association between an adjective and a certain gender could be the accomplice in forming gender stereotypes (Menegatti and Rubini, 2017). If these stereotypes are learned by the adjective word embeddings, they would be propagated to downstream NLP applications; accordingly, the gender stereotypes would be reinforced in users' mind. For example, the system will tend to use "smart" to describe males because of the existed social stereotype in training data that males are good at mathematics; then, the influence of the stereotype would be spread and increased again. Thus, we want to further investigate the bias encoded by the embeddings and how they are different with what in people's mind.