Gender Bias Hidden Behind C hinese Word Embeddings: The Case of C hinese Adjectives (2021)

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.

## introduction in the deep learning era, a major area of nlp has concerned the representation of words in lowdimensional and continuous vector spaces. people propose different algorithms to achieve such goal, including word2vec @xcite , glove @xcite and fasttext @xcite . word embeddings play an important role in many nlp tasks, such as machine translation @xcite and sentiment analysis @xcite . however, several studies point out that word embeddings could capture the gender stereotypes in training data and transmit them to downstream applications @xcite . the consequence is often unbearable. take machine translation as an example, if we translate a sentence concerning "nurse" from a language with gender-neutral pronouns to english, a female pronoun might be automatically produced to denote "nurse" @xcite . undoubtedly, this falls into the trap of the typical gender stereotypes. therefore, the investigation of gender bias in word embeddings is necessary and accordingly attracts scholars' attention in recent years @xcite . most previous studies concerning gender bias in word embeddings only take english as the target language. other languages are only included in several multi-lingual projects. for example, @xcite evaluate the gender bias in machine translation by translating 12 gender neutral languages with the google translate api; @xcite examine whether gender stereotypes could be reflected in the large-scale distributional structure of 25 natural languages. apart from english, other languages have rarely been the target language in the research under this topic. this paper will take chinese as the target language, investigating gender bias in word embeddings trained with the model designed for special features of chinese. the fact that social stereotypes are conveyed in our language is often neglected by the public. from the commonly used adjectives, we could get a glimpse of the social stereotypes of a certain group of people. these stereotypes would confine us to what we should be in the minds of the public. it has been confirmed that when describing different genders, people will choose divergent groups of adjectives even though such a choice might change with the development of society @xcite . therefore, this study focuses on the problem of gender stereotypes from the perspective of adjectives. by scoring the gender bias from our trained vectors, we yield a subjective result of the gender preference of a set of adjectives. through comparing our results with a handcrafted data set of human attitudes towards adjectives @xcite , we find that what is encoded in word embeddings is, to some extent, inconsistent with people's feelings on the gender preference of these adjectives. ## related work gender could affect the usage of adjectives @xcite . on the other hand, the attitude of the public towards the social roles of men and women could also be indicated by how adjectives correlates with genders @xcite . in the past decade, an increasing number of studies investigating adjectives and gender stereotypes from various perspectives are proposed and developed. @xcite reveals the stereotype in the description of boys and girls by analyzing adjectives only used for a certain gender with the aid of corpora covering a range of written genres. research of bollywood movies @xcite finds that different adjectives are chosen when they try to create impressive male and female roles. the significant divergence between the usage of adjectives for describing men and women has also been confirmed by @xcite , and they also notice the variance is consistent with common stereotypes. @xcite trace the change of gender bias in chinese adjectives based on a handcrafted data set that consists of the gender preference score of adjectives. however, the number of studies focusing on chinese adjectives and gender bias is still limited. gender bias in word embeddings and the corresponding debiasing methods have been a vivid re-search field in recent years. @xcite and @xcite confirm that word embedding models could precisely capture the social stereotypes concerning people's careers, such as the relationship in an analogy that man is to computer programmer as woman is to homemaker. this bias could even be amplified by embedding models @xcite . besides english, other target languages like swedish @xcite and dutch @xcite gradually attract the attention of researchers. various methods for assessing bias and debiasing are proposed and developed in previous studies. @xcite firstly measure the gender bias by computing the projection of a word on he -she direction, which has been confirmed strongly correlated with the public judgment of gender stereotypes. based on this method, they also develop a debiasing method by post-processing the generated word vectors. @xcite further propose to debias word embeddings in training procedure by changing the loss of glove model @xcite and employing an adversarial network, respectively. despite a large amount of research having been done in this field, to the best of our knowledge, no one has assessed the underlying gender bias behind adjectives, especially those in non-english languages. to complement the full picture of gender bias encoded in word representation, this paper examines the problem from the perspective of adjectives rather than nouns of occupations that repeatedly appeared in previous studies. based on the human scoring data set of @xcite , we investigate the similarities and differences between the automatically captured gender bias in chinese and people's judgement. ## methodology to uncover the gender stereotypes conveyed by adjectives, we first preprocess a corpus of online chinese news and train word embeddings on it with two different models. then, we calculate the gender bias scores based on the generated two vectors and compare them with the human scoring data set, adjectives list with gendered skewness and sentiment (agss) @xcite . ## conclusion in this paper, we investigate gender bias in chinese word embeddings from the perspective of adjectives, and compare automatically calculated gender bias score with human attitudes. we elaborately present the differences between gender bias encoded in word vectors and the people's feeling of the same adjective. for the words that people believe for describing women, the extracted score of gender bias gives an identified results; while for adjectives that should be used for men in people's mind, our results suggest that these group of words are actually more neutral than the crowd judgement. additionally, how the word embedding models covering character-level information perform in terms of capturing gender bias in chinese is also examined.