@article{chen-etal-2026-munichus,
    title = "{MUNIC}hus: {MU}ltilingual News Image Captioning Benchmark",
    author = "Chen, Yuji  and
      Plum, Alistair  and
      Hettiarachchi, Hansi  and
      Kanojia, Diptesh  and
      Basnet, Saroj  and
      Zampieri, Marcos  and
      Ranasinghe, Tharindu",
    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.708/",
    pages = "9008--9017",
    abstract = "The goal of news image captioning is to generate captions by integrating news article content with corresponding images, highlighting the relationship between textual context and visual elements. The majority of research on news image captioning focuses on English, primarily because datasets in other languages are scarce. To address this limitation, we release the first multilingual news image captioning benchmark, MUNIChus, comprising 9 languages, including several low-resource languages such as Sinhala and Urdu. We evaluate various state-of-the-art neural news image captioning models on MUNIChus and find that news image captioning remains challenging. We also make MUNIChus publicly available as a public leaderboard with over 20 models already benchmarked. We hope that MUNIChus will enable further advancements in developing and evaluating multilingual news image captioning models."
}