Why Low-Resource NLP Needs More Than Cross-Lingual Transfer: Lessons Learned from Luxembourgish
Year: 2026
Type: conference-paper
Abstract: 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 targetlanguage 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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7166857715