Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings (2020)

Abstract: Word embeddings are the standard model for semantic and syntactic representations of words. Unfortunately, these models have been shown to exhibit undesirable word associations resulting from gender, racial, and religious biases. Existing post-processing methods for debiasing word embeddings are unable to mitigate gender bias hidden in the spatial arrangement of word vectors. In this paper, we propose RAN-Debias, a novel gender debiasing methodology that not only eliminates the bias present in a word vector but also alters the spatial distribution of its neighboring vectors, achieving a bias-free setting while maintaining minimal semantic offset. We also propose a new bias evaluation metric, Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections. Experiments based on a suite of evaluation metrics show that RAN-Debias significantly outperforms the state-of-the-art in reducing proximity bias (GIPE) by at least 42.02%. It also reduces direct bias, adding minimal semantic disturbance, and achieves the best performance in a downstream application task (coreference resolution).

## introduction word embedding methods @xcite @xcite have been staggeringly successful in mapping the semantic space of words to a space of real-valued vectors, capturing both semantic and syntactic relationships. however, as recent research has shown, word embeddings also possess a spectrum of biases related to gender @xcite , race, and religion @xcite . @xcite showed that there is a disparity in the association of professions with gender. for instance, while women are associated more closely with ''receptionist'' and ''nurse'', men are associated more closely with ''doctor'' and ''engineer''. similarly, a word embedding model trained on data from a popular social media platform generates analogies such as ''muslim is to terrorist as christian is to civilian'' @xcite . therefore, given the large scale use of word embeddings, it becomes cardinal to remove the manifestation of biases. in this work, we focus on mitigating gender bias from pre-trained word embeddings. as shown in to account for these problems, we propose a post-processing based debiasing scheme for noncontextual word embeddings, called ran-debias (repulsion, attraction, and neutralization based debiasing). ran-debias not only minimizes the projection of gender-biased word vectors on the gender direction but also reduces the semantic similarity with neighboring word vectors having illicit proximities. we also propose kbc (knowledge based classifier), a word classification algorithm for selecting the set of words to be debiased. kbc utilizes a set of existing lexical knowledge bases to maximize classification accuracy. additionally, we propose a metric, gender-based illicit proximity estimate (gipe), which quantifies gender bias in the embedding space resulting from the presence of illicit proximities between word vectors. we evaluate debiasing efficacy on various evaluation metrics. for the gender relational analogy test on the sembias dataset @xcite , ran-glove (ran-debias applied to glove word embedding) outperforms the next best baseline gn-glove (debiasing method proposed by @xcite ) by 21.4% in genderstereotype type. ran-debias also outperforms the best baseline by at least 42.02% in terms of gipe. furthermore, the performance of ran-glove on word similarity and analogy tasks on a number of benchmark datasets indicates the addition of minimal semantic disturbance. in short, our major contributions foot_0 can be summarized as follows: in the following subsections, we discuss existing gender debiasing methods based on their mode of operation. methods that operate on pre-trained word embeddings are known as post-processing methods, and those which aim to retrain word embeddings by either introducing corpus-level changes or modifying the training objective are known as learning-based methods. ## experiment results we conduct the following performance evaluation tests: although we report and analyze the performance of ran-glove in our experiments, we also applied ran-debias to other popular noncontextual and monolingual word embedding, word2vec @xcite to create ran-word2vec. as expected, we observed similar results (hence not reported for the sake of brevity), emphasizing the generality of ran-debias. note that the percentages mentioned in the rest of the section are relative unless stated otherwise. ## conclusion in this paper, we proposed a post-processing gender debiasing method called ran-debias. our method not only mitigates direct bias of a word but also reduces its associations with other words that arise from gender-based predilections. we also proposed a word classification method, called kbc, for identifying the set of words to be debiased. instead of using ''biased'' word embeddings, kbc uses multiple knowledge bases for word classification. moreover, we proposed gender-based illicit proximity estimate (gipe), a metric to quantify the extent of illicit proximities in an embedding. ran-debias significantly outperformed other debiasing methods on a suite of evaluation metrics, along with the downstream application task of coreference resolution while introducing minimal semantic disturbance. in the future, we would like to enhance kbc by utilizing machine learning methods to account for the words which are absent in the knowledge base. currently, ran-debias is directly applicable to non-contextual word embeddings for nongendered grammatical languages. in the wake of recent work such as @xcite , we would like to extend our work towards contextualized embedding models and other languages with grammatical gender like french and spanish.