Gender Issues in Machine Translation

Abstract

: In this paper, this study investigates gender bias in MT focusing on three generic, easily accessible, and widely distributed MT systems, i.e. DeepL, Google Translate and e-Translation, in the EL-EN, EN-EL, DE-EL, and DE-EN language pairs. Regarding the pairs EN-EL and EL-EN, ten texts are used from two different genres and various domains (5 journalistic articles and 5 administrative/institutional texts) with varying degrees of inclusive language and with the aim of establishing: a) whether the three systems perform differently as regards gender bias, b) whether the systems perform differently in the two language pairs EL-EN, EN-EL and c) whether the use of inclusive language in the source text influences the MT output and can thus be used as a means to mitigate MT gender bias. Regarding the language pairs DE-EL and DE-EN, the intention is to illuminate the use of neutral-gender language in one segment of a political article, one segment of an official law text of the EU, and one segment of an official text of the German Federal Ministry of Education and Research (BMBF). Τhe text segments range from 200 to 300 words. German, as a grammatically gendered language, has both semantic and formal (grammatical) gender, which is reflected not only in nouns, but in adjectives, adverbs, and articles. The issues studied concern gender inclusivity and gender discrimination. They remain consistent across the texts selected.