Recent advancements in neural machine translation (NMT) have revolutionized the field, yet the dependency on extensive parallel corpora limits progress for low-resource languages. Cross-lingual transfer learning offers a promising solution by utilizing data from high-resource languages but often struggles with in-domain NMT. In this paper, we investigate three pivotal aspects: enhancing the domain-specific quality of NMT by fine-tuning domain-relevant data from different language pairs, identifying which domains are transferable in zero-shot scenarios, and assessing the impact of language-specific versus domain-specific factors on adaptation effectiveness. Using English as the source language and Spanish for fine-tuning, we evaluate multiple target languages including Portuguese, Italian, French, Czech, Polish, and Greek. Our findings reveal significant improvements in domain-specific translation quality, especially in specialized fields such as medical, legal, and IT, underscoring the importance of well-defined domain data and transparency of the experiment setup in in-domain transfer learning.

本研究解决了低资源语言机器翻译中因依赖大量平行语料而造成的进展限制问题。通过细化领域相关数据，本文提出了一种创新方法，在零样本场景下识别可迁移领域，并评估语言特定与领域特定因素对适应有效性的影响。研究表明，在医学、法律和信息技术等专业领域，明确定义的领域数据显著提升了翻译质量，强调了其在领域迁移学习中的重要性。

定义边界：领域规范对机器翻译中跨语言和跨领域迁移的影响