为何低资源NLP需要比跨语言迁移更多：从卢森堡语学到的教训
Why Low-Resource NLP Needs More Than Cross-Lingual Transfer: Lessons Learned from Luxembourgish
AI总结：
本文探讨了低资源语言处理中跨语言迁移与语言特定努力的互补性，指出两者需结合以构建可持续的NLP流程。
摘要：
跨语言迁移已成为扩展自然语言处理（NLP）技术到低资源语言的核心范式。通过利用高资源语言的监督，多语言语言模型可以在极少或没有目标语言标注数据的情况下实现强大任务性能。然而，跨语言迁移在多大程度上可以替代语言特定的努力仍不清楚。本文综合了卢森堡语的先前研究发现和数据收集结果，该语言尽管在语言类型上接近高资源语言，并处于多语言环境中，但在现代NLP技术中仍代表性不足。研究发现跨语言迁移与语言特定努力之间存在根本的相互依赖关系。跨语言迁移可以显著提高目标语言性能，但其成功依赖于足够高质量且任务对齐的目标语言数据的可用性。同时，此类资源在低资源环境下通常规模太小，无法单独驱动强大性能。相反，这些资源只有在跨语言框架中被利用时才能发挥全部潜力。因此，本文认为跨语言迁移和语言特定努力不应被视为竞争替代品，而是可持续低资源NLP流程的互补组成部分。基于这些见解，本文提供了将跨语言迁移与语言特定开发整合和平衡的实用指南。
英文摘要：
Cross-lingual transfer has become a central paradigm for extending natural language processing (NLP) technologies to low-resource languages. By leveraging supervision from high-resource languages, multilingual language models can achieve strong task performance with little or no labeled target-language data. However, it remains unclear to what extent cross-lingual transfer can substitute for language-specific efforts. In this paper, we synthesize prior research findings and data collection results on Luxembourgish, which, despite its typological proximity to high-resource languages and its presence in a multilingual context, remains insufficiently represented in modern NLP technologies. Across findings, we observe a fundamental interdependence between cross-lingual transfer and language-specific efforts. Cross-lingual transfer can substantially improve target-language performance, but its success depends critically on the availability of sufficiently high-quality, task-aligned target-language data. At the same time, such resources, particularly in low-resource settings, are typically too limited in scale to drive strong performance on their own. Instead, such resources reach their full potential only when leveraged within a cross-lingual framework. We therefore argue that cross-lingual transfer and language-specific efforts should not be viewed as competing alternatives. Instead, they function as complementary components of a sustainable low-resource NLP pipeline. Based on these insights, we provide practical guidelines for integrating and balancing cross-lingual transfer with language-specific development in sustainable low-resource NLP pipelines.
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