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Automatic detection of (potential) factors in the source text leading to gender bias in machine translation

Publication type
U
Publication status
Published
Authors
Hackenbuchner, J., Daems, J, & Tezcan, A.
Series
Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)
Publisher
European Association for Machine Translation (Sheffield, United Kingdom)
Conference
The 25th Annual Conference of The European Association for Machine Translation (Sheffield, United Kingdom)
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Abstract

This research project aims to develop a comprehensive methodology to help make machine translation (MT) systems more gender-inclusive for society. The goal is the creation of a detection system, a machine learning (ML) model trained on manual annotations, that can automatically analyse source data and detect and highlight words and phrases that influence the gender bias inflection in target translations.The main research outputs will be (1) a manually annotated dataset, (2) a taxonomy, and (3) a fine-tuned model.