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 of bilingual models without consideration for multilingual
systems. In this paper, we specifically target the unambiguous gender bias
issue of multilingual machine translation models and propose a new mitigation
method based on a novel perspective on the problem. We hypothesize that the
gender bias in unambiguous settings is due to the lack of gender information
encoded into the non-explicit gender words and devise a scheme to encode
correct gender information into their latent embeddings. Specifically, we
employ Gender-Aware Contrastive Learning, GACL, based on gender pseudo-labels
to encode gender information on the encoder embeddings. Our method is
target-language-agnostic and applicable to already trained multilingual machine
translation models through post-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.

针对多语言机器翻译模型中明显的性别偏见问题，提出了一种新的缓解方法，Gender-Aware Contrastive Learning，通过伪标签在编码器嵌入中编码性别信息来提高性别准确度并改善其他目标语言的性别准确度。