Can Existing Methods Debias Languages Other than E nglish? First Attempt to Analyze and Mitigate J apanese Word Embeddings (2020)

Abstract: It is known that word embeddings exhibit biases inherited from the corpus, and those biases reflect social stereotypes. Recently, many studies have been conducted to analyze and mitigate biases in word embeddings. Unsupervised Bias Enumeration (UBE) (Swinger et al., 2019) is one of approach to analyze biases for English, and Hard Debias (Bolukbasi et al., 2016) is the common technique to mitigate gender bias. These methods focused on English, or, in smaller extent, on Indo-European languages. However, it is not clear whether these methods can be generalized to other languages. In this paper, we apply these analyzing and mitigating methods, UBE and Hard Debias, to Japanese word embeddings. Additionally, we examine whether these methods can be used for Japanese. We experimentally show that UBE and Hard Debias cannot be sufficiently adapted to Japanese embeddings.

## introduction word embeddings are widely used in natural language processing tasks, and they have been reported to inherit social stereotypes, e.g. gender and racial stereotypes @xcite . for example, "programmer" and "homemaker" should be gender neutral by definition, but the analogy of "man is to programmer as woman is to homemaker" holds as observed by @xcite . such biases cause differences in f1 scores between the pro-and anti-stereotypical conditions. for example in the coreference resolution task, it is difficult to correctly link "physician:she" and "secretary:he" for systems which use gender-biased word embeddings, because "physician:he" and "secretary:she" are strongly related more than "physician:she" and "secretary:he" in the word embeddings @xcite . therefore, in recent years, research has been conducted to mitigate the bias in word embeddings @xcite @xcite . however, to the authors' best knowledge, most of them have focused on english @xcite , and no study has addressed word embeddings of languages other than indo-european languages about bias analysis and mitigation. we hypothesize that it is not obvious that the method developed for english can be easily adapted to other languages for two following reasons. first is due to various grammatical features which do not exist in english. embeddings can have different characteristics depending on language, for example spanish words have gender which leads to the grammatical gender bias @xcite . there is a substantial risk that we cannot adapt the bias mitigation methods meant for english while working on such a language. secondly, especially when the language family differs, not only the characteristics of a given language but also the cultural background of its users changes, which in turn influences further the bias in the embeddings @xcite . therefore, it may not be possible to directly apply bias analysis and mitigation methods developed for english to other languages. bias statement following categorization of crawford (2017), we focus on representational bias, especially stereotyping one, which means that a system "propagates negative generalisations about particular social groups" @xcite . stereotyping happens in natural language processing tasks when an unfair association of words represents a particular social group with other concepts (not included in its definition), like an analogy of "man is to programmer as woman is to homemaker". if an ai agent has such stereotypes, they can appear in its output as reported in works on dialogue systems @xcite , possibly harming users. there are several works on stereotypes in word embeddings for english @xcite @xcite and some other languages @xcite , but to the authors' best knowledge, research regarding japanese word embeddings does not exist. in this paper, we analyze the representational bias in japanese word embeddings, and attempt to mitigate gender bias by using existing methods designed for english. we also show that those methods are difficult to generalize to japanese. ## specificity of japanese language japanese and western languages use different types of characters. there are three types of characters in japanese language: phonetic hiragana * , katakana, and ideographic kanji. embeddings of kanji may capture not only the meaning of the word but also the meaning of the characters. for example word 数学 ("maths") consists of two ideograms: 数 ("number") and 学 ("learning"). katakana often represents a foreign word プログラマ ("programmer"), while words written in rounded shape of hiragana like ふわ ふわ (fuwafuwa, "fluffy") are often associated with a feminine image @xcite . ## experiments in this section we explain word embeddings we used, describe ube @xcite used in the bias analysis experiment, and two other methods (hard debias @xcite , double-hard debias @xcite ) used in the bias mitigation experiment. finally, we explain our evaluation methodology. ## conclusion in this paper, we analyzed the representational bias of japanese word embeddings and attempted to mitigate the gender bias in these embeddings with previous methods developed for english. the experimental results showed that japanese first name embeddings do not include social stereotypes and that the similarity of word vectors is influenced by the superficial information of character type. and, the existing gender ¶ unfortunately, there is no pre-trained glove model available for japanese, so we were not able to investigate the influence of the embedding type.