@inproceedings{goncalves-etal-2026-luxdiagrc,
    title = "{L}ux{D}iag{RC}: A Diagnostic Reading Comprehension Corpus for {L}uxembourgish with Linguistic and Cognitive Annotation Layers",
    author = "Gon{\c{c}}alves, Christophe Friezas  and
      Lamsiyah, Salima  and
      Schommer, Christoph",
    editor = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu  and
      Plum, Alistair  and
      Rayson, Paul  and
      Mitkov, Ruslan  and
      Gaber, Mohamed  and
      Premasiri, Damith  and
      Tan, Fiona Anting  and
      Uyangodage, Lasitha",
    booktitle = "Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({L}o{R}es{LM} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.loreslm-1.46/",
    pages = "532--541",
    ISBN = "979-8-89176-377-7",
    abstract = "Reading comprehension resources for low-resource languages remain limited, particularly datasets designed for educational assessment and diagnostic analysis in contrast to binary correctness.We present a diagnostically rich reading comprehension corpus forLuxembourgish, annotated using a two-layer framework that separateslinguistic sources of textual difficulty from cognitive and diagnosticproperties of comprehension questions. The linguistic layer captures span-level lexical, syntactic, morphological, and discourse-related features, while the cognitive layerannotates multiple-choice questions according to the PIRLS cognitiveprocesses and diagnostically meaningful distractor types following theSTARC framework.This design enables fine-grained analysis of reading comprehensionerrors by linking response patterns to underlying linguistic phenomena. The resulting corpus consists of 640 multiple-choice questions based on 16 annotated Luxembourgish texts. We describe the annotation methodology agreement measures, and will releasethe dataset as a publicly available resource for educational andlow-resource NLP research."
}