多语言自然语言处理中的跨语言迁移：语系与形态学的作用
Cross-Linguistic Transfer in Multilingual NLP: The Role of Language Families and Morphology
AI总结：
该研究从语系与形态学视角探究多语言NLP的跨语言迁移，分析语系亲缘度、形态相似性及语言距离对迁移效果的影响，并梳理融入类型学与形态信息的预训练新兴方法。
摘要：
跨语言迁移已成为多语言NLP的关键方向，它能让在资源丰富语言上训练的模型更有效地应用于低资源语言。近期，大规模多语言预训练语言模型（如mBERT、XLM-R）展现出强大的零样本迁移能力[14][13]。本文从语系和形态学的视角研究跨语言迁移，探究语系亲缘度和形态相似性如何影响不同NLP任务的性能。我们进一步讨论了研究结果，以及其与近期文献中发现的关联。整体而言，我们对比了多语言模型的性能，梳理了语言距离指标与迁移效果的相关性。我们还探讨了将类型学和形态学信息融入模型预训练以提升向多样语言迁移能力的新兴方法[18][19]。
英文摘要：
Cross-lingual transfer has become a crucial aspect of multilingual NLP, as it allows for models trained on resource-rich languages to be applied to low-resource languages more effectively. Recently massively multilingual pre-trained language models (e.g., mBERT, XLM-R) demonstrate strong zero-shot transfer capabilities[14] [13]. This paper investigates cross-linguistic transfer through the lens of language families and morphology. Investigating how language family proximity and morphological similarity affect performance across NLP tasks. We further discuss our results and how it relates to findings from recent literature. Overall, we compare multilingual model performance and review how linguistic distance metrics correlate with transfer outcomes. We also look into emerging approaches that integrate typological and morphological information into model pre-training to improve transfer to diverse languages[18] [19].
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。
