@inproceedings{philippy-etal-2026-low,
    title = "Why Low-Resource {NLP} Needs More Than Cross-Lingual Transfer: Lessons Learned from {L}uxembourgish",
    author = "Philippy, Fred  and
      Guo, Siwen  and
      Klein, Jacques  and
      Bissyand{\'e}, Tegawend{\'e} F.",
    editor = "Elazar, Yanai  and
      Ettinger, Allyson  and
      Kassner, Nora  and
      Ruder, Sebastian",
    booktitle = "Proceedings of The Big Picture v2: Crafting a Research Narrative",
    month = jul,
    year = "2026",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bigpicture-main.7/",
    pages = "82--93",
    ISBN = "979-8-89176-416-3",
    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 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."
}