目标语言无关的性别感知对比学习用于缓解多语言机器翻译中的偏见
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation
AI总结：
针对多语言机器翻译中的性别偏见，提出目标语言无关的性别感知对比学习（GACL），通过微调预训练模型提升性别准确性且不损害翻译性能，并验证其跨语言迁移性和模型规模适用性。
摘要：
性别偏见是机器翻译中的一个重要问题，促使研究人员持续开发偏见缓解技术。然而，大多数工作专注于对双语模型进行去偏，而很少考虑多语言系统。在本文中，我们专门针对多语言机器翻译模型在存在单一正确翻译的明确情况下的性别偏见问题，并提出一种基于新颖方法的偏见缓解方法。具体来说，我们提出了性别感知对比学习（Gender-Aware Contrastive Learning, GACL），该方法将上下文性别信息编码到非显式性别词的表示中。我们的方法是目标语言无关的，并可通过微调应用于预训练的多语言机器翻译模型。通过多语言评估，我们表明我们的方法在不损害翻译性能的情况下大幅提高了性别准确性。我们还观察到，融入的性别信息能够迁移并有益于其他目标语言的性别准确性。最后，我们证明了我们的方法适用于不同规模的模型并对其有益。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
