Gender Bias Hidden Behind Chinese Word Embeddings: The Case of Chinese Adjectives
It addresses gender bias in Chinese NLP, an understudied area, but is incremental as it applies existing methods to a new language and word type.
The paper investigates gender bias in Chinese adjective word embeddings, comparing bias in embeddings to human attitudes using a human-scored dataset.
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.