G e FMT : Gender-Fair Language in G erman Machine Translation (2024)

Abstract: Research on gender bias in Machine Translation (MT) predominantly focuses on binary gender or few languages. In this project, we investigate the ability of commercial MT systems and neural models to translate using gender-fair language (GFL) from English into German. We enrich a community-created GFL dictionary, and sample multi-sentence test instances from encyclopedic text and parliamentary speeches. We translate our resources with different MT systems and open-weights models. We also plan to post-edit biased outputs with professionals and share them publicly. The outcome will constitute a new resource for automatic evaluation and modeling gender-fair EN-DE MT.

## background a wealth of research in the field of machine translation (mt) focuses on gender bias @xcite . however, most recent efforts predominantly focus on binary gender only @xcite or few languages. for instance, first contributions benchmark genderneutral language in english-to-italian mt @xcite . gender-neutrality refers to avoiding gender-specific elements, often by rewording sentences with indefinite pronouns and passive constructions, amongst others. in contrast to gender-neutrality, new proposals advocate for gender-inclusive translation, e.g., by accounting for neopronouns (e.g., xe/xem), neomorphemes and characters such as the gender star (*) in german (e.g., der*die berater*in). such approaches increase gender visibility and might therefore be preferred for reference to non-binary individuals. ## project overview the present project is a one-year joint effort of the university of graz and hamburg, and the instituto de telecomunicac ¸ões in lisbon. it encompasses (i) the creation of a dataset we will share with the community for gender-fair mt, foot_0 (ii) human and automatic assessment of the use (or lack thereof) of gender-fair language in different commercial and non-commercial mt systems, and (iii) test new methods to generate translations based on gender-fair post-edited datasets. these steps are detailed in the following paragraphs. we collect an additional set of english passages that mention our dictionary entries. we sample sentences from europarl @xcite and wikipedia. 3 europarl is a widely recognized benchmark dataset for mt displaying institutional language from parliamentary speechesperhaps amongst the first contexts gfl was designed for @xcite . wikipedia presents encyclopedic text, opening to new contexts where our seed nouns appear. ## expected outcome this project will produce the following openlyaccessible resources to the community: i) a new human-curated dictionary of english nouns with their german gender-fair inflections. ii) a parallel en-de corpus with source sentences from wikipedia and european parliament speeches that mention the dictionary nouns, and hypothesis sentences automatically translated with several stateof-the-art systems. iii) a new human-curated corpus of gender-fair german translations of the sentences above, obtained via post-editing machinegenerated translations. taken together, these resources will constitute the largest collection for studying automatic gender-fair translation from english into german.