GATE X-E : A Challenge Set for Gender-Fair Translations from
  Weakly-Gendered Languages
Spencer Rarrick Ranjita Naik Sundar Poudel Vishal Chowdhary
Neural Machine Translation (NMT) continues to improve in quality and
adoption, yet the inadvertent perpetuation of gender bias remains a significant
concern. Despite numerous studies on gender bias in translations into English
from weakly gendered-languages, there are no benchmarks for evaluating this
phenomenon or for assessing mitigation strategies. To address this gap, we
introduce GATE X-E, an extension to the GATE (Rarrick et al., 2023) corpus,
that consists of human translations from Turkish, Hungarian, Finnish, and
Persian into English. Each translation is accompanied by feminine, masculine,
and neutral variants. The dataset, which contains between 1250 and 1850
instances for each of the four language pairs, features natural sentences with
a wide range of sentence lengths and domains, challenging translation rewriters
on various linguistic phenomena. Additionally, we present a translation gender
rewriting solution built with GPT-4 and use GATE X-E to evaluate it. We open
source our contributions to encourage further research on gender debiasing.