  Gender bias is a significant issue in machine translation, leading to ongoing
research efforts in developing bias mitigation techniques. However, most works
focus on debiasing bilingual models without much consideration for multilingual
systems. In this paper, we specifically target the gender bias issue of
multilingual machine translation models for unambiguous cases where there is a
single correct translation, and propose a bias mitigation method based on a
novel approach. Specifically, we propose Gender-Aware Contrastive Learning,
GACL, which encodes contextual gender information into the representations of
non-explicit gender words. Our method is target language-agnostic and is
applicable to pre-trained multilingual machine translation models via
fine-tuning. Through multilingual evaluation, we show that our approach
improves gender accuracy by a wide margin without hampering translation
performance. We also observe that incorporated gender information transfers and
benefits other target languages regarding gender accuracy. Finally, we
demonstrate that our method is applicable and beneficial to models of various
sizes.
