研究语言亲缘关系与形态相似性如何影响多语言模型跨语言迁移效果
Cross-Linguistic Transfer in Multilingual NLP: The Role of Language Families and Morphology
- 基于语言家族和形态特征分析跨语言迁移能力
- 发现语言相似性越高，迁移效果越显著
- 适合关注多语言模型泛化能力的研究者
跨语言迁移已成为多语言自然语言处理的关键，使在资源丰富语言上训练的模型能更有效地应用于低资源语言。近年来的大规模多语言预训练模型（如mBERT、XLM-R）展现出强大的零样本迁移能力。本文从语言家族和形态学角度探究跨语言迁移，分析语言亲缘关系与形态相似性对各类NLP任务性能的影响。我们对比了多语言模型的表现，并考察语言距离度量与迁移结果的相关性。此外，还探讨了将类型学与形态信息融入模型预训练以提升对多样化语言迁移效果的新兴方法。
原文摘要 · Abstract (English)
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].
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