Gender-neutral Language Use in the Context of Gender Bias in Machine Translation (A Review Literature)
DOI:
https://doi.org/10.33919/JCAL.23.1.5Keywords:
gender bias, machine translation, NLP tools, gender-neutral language use, non-binary genderAbstract
Gender bias has become one of the central issues analysed within natural language processing (NLP) research. A main concerns in this field relates to the fact that many NLP tools and automatic machine learning systems not only reflect, but also reinforce social disparities, including those related to gender, and language technology is one of the areas in which this issue is pronounced. This paper analyses the problem of gender-neutral language use from the standpoint of gender bias in machine translation (MT). We determine which types of harms can be caused by the failure to reflect gender-neutral language in translation, provide the general definition of gender bias in MT, describe its sources and provide an overview of existing mitigating strategies. One of the main contributions of this work is that it focuses not only on females, but also non-binary people, whose linguistic visibility has been receiving only limited attention from academia. This literature review provides a firm foundation for further research in this area aimed at addressing the problem of gender bias in machine translation, especially bias linked to representational harms.
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