  We study the effect of tokenization on gender bias in machine translation, an
aspect that has been largely overlooked in previous works. Specifically, we
focus on the interactions between the frequency of gendered profession names in
training data, their representation in the subword tokenizer's vocabulary, and
gender bias. We observe that female and non-stereotypical gender inflections of
profession names (e.g., Spanish "doctora" for "female doctor") tend to be split
into multiple subword tokens. Our results indicate that the imbalance of gender
forms in the model's training corpus is a major factor contributing to gender
bias and has a greater impact than subword splitting. We show that analyzing
subword splits provides good estimates of gender-form imbalance in the training
data and can be used even when the corpus is not publicly available. We also
demonstrate that fine-tuning just the token embedding layer can decrease the
gap in gender prediction accuracy between female and male forms without
impairing the translation quality.
