@article{alhafni-etal-2026-bilingual,
    title = "A Bilingual Bimodal Benchmark for {A}rabic-{E}nglish {NLP} across Grammatical Correction, Essay Scoring, Morphological Tagging, and Speech Recognition",
    author = "Alhafni, Bashar  and
      Hamed, Injy  and
      Eryani, Fadhl  and
      Palfreyman, David  and
      Habash, Nizar",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.137/",
    pages = "1732--1749",
    abstract = "Building comprehensive datasets that support a variety of NLP tasks and cover a diversity of languages and domains is vital for NLP evaluation purposes. In this paper, we present ZAEBUC*, a dataset that builds upon and enriches prior corpora with new annotations and benchmarking experiments. ZAEBUC* serves as a benchmark for a range of NLP tasks, including grammatical error correction, automated essay scoring, automatic speech recognition, and morphological tagging, which includes tokenization, part-of-speech tagging, and lemmatization. The dataset covers Arabic and English in both written and spoken forms, offering a bilingual and bimodal resource. Furthermore, the corpus brings together a collection of resources gathered from a similar population, enabling cross-linguistic and cross-modal comparisons. We provide benchmarking results, demonstrating the performance of NLP models, including LLMs, across various tasks, languages, and modalities."
}