Gender Lost In Translation: How Bridging The Gap Between Languages
  Affects Gender Bias in Zero-Shot Multilingual Translation
Lena Cabrera Jan Niehues
Neural machine translation (NMT) models often suffer from gender biases that
harm users and society at large. In this work, we explore how bridging the gap
between languages for which parallel data is not available affects gender bias
in multilingual NMT, specifically for zero-shot directions. We evaluate
translation between grammatical gender languages which requires preserving the
inherent gender information from the source in the target language. We study
the effect of encouraging language-agnostic hidden representations on models'
ability to preserve gender and compare pivot-based and zero-shot translation
regarding the influence of the bridge language (participating in all language
pairs during training) on gender preservation. We find that language-agnostic
representations mitigate zero-shot models' masculine bias, and with increased
levels of gender inflection in the bridge language, pivoting surpasses
zero-shot translation regarding fairer gender preservation for speaker-related
gender agreement.