The study investigates gender bias in machine translations by analyzing pronoun selection. Different languages show varying patterns of pronoun use, highlighting the need to work with multiple languages for generalizable results. The UCA metric is proposed to assess uncertainty of gender in translations, showing robustness across languages. Verbs are identified as drivers of gender uncertainty, with significant differences observed. Changes in the DeepL API behavior over time impact pronoun usage but not UCA values, indicating stability. Future research includes exploring more translation APIs and longer text fragments for analysis.
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by Peter J Barc... at arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.11896.pdfDeeper Inquiries