标题： 机器翻译迁移学习的跨语言考察 显示英文标题

标题： Cross-Linguistic Examination of Machine Translation Transfer Learning

摘要： 本研究通过评估五种不同的语言对，调查了迁移学习在多种不同语言家族中的机器翻译有效性。 利用高资源语言上的预训练模型，这些模型在低资源语言上进行了微调，考察了超参数（如学习率、批量大小、训练轮数和权重衰减）的变化。 研究涵盖了来自不同语言背景的语言对：闪米特语系（现代标准阿拉伯语 - 黎凡特阿拉伯语）、班图语系（豪萨语 - 祖鲁语）、罗曼语系（西班牙语 - 加泰罗尼亚语）、斯拉夫语系（斯洛伐克语 - 马其顿语）以及语言孤立现象（东 Armenian - 西 Armenian）。 结果表明，迁移学习在不同语言家族中均有效，尽管超参数的影响有所不同。通常而言，适中的批量大小（例如 32）更为有效，而非常高的学习率可能会破坏模型的训练。 该研究强调了迁移学习在多语言环境中的普遍性，并建议一致的超参数设置可以简化并提高多语言模型训练的效率。 显示英文摘要

摘要： This study investigates the effectiveness of transfer learning in machine translation across diverse linguistic families by evaluating five distinct language pairs. Leveraging pre-trained models on high-resource languages, these models were fine-tuned on low-resource languages, examining variations in hyperparameters such as learning rate, batch size, number of epochs, and weight decay. The research encompasses language pairs from different linguistic backgrounds: Semitic (Modern Standard Arabic - Levantine Arabic), Bantu (Hausa - Zulu), Romance (Spanish - Catalan), Slavic (Slovakian - Macedonian), and language isolates (Eastern Armenian - Western Armenian). Results demonstrate that transfer learning is effective across different language families, although the impact of hyperparameters varies. A moderate batch size (e.g., 32) is generally more effective, while very high learning rates can disrupt model training. The study highlights the universality of transfer learning in multilingual contexts and suggests that consistent hyperparameter settings can simplify and enhance the efficiency of multilingual model training.