@inproceedings{tsoneva-etal-2026-selective,
    title = "Selective Multimodal Retrieval for Automated Verification of Image{--}Text Claims",
    author = "Tsoneva, Yoana  and
      Feig, Paul-Conrad  and
      Li, Jiaao  and
      Solopova, Veronika  and
      Foroutan, Neda  and
      Hilbert, Arthur  and
      Schmitt, Vera",
    editor = "Akhtar, Mubashara  and
      Aly, Rami  and
      Cao, Rui  and
      Christodoulopoulos, Christos  and
      Cocarascu, Oana  and
      Guo, Zhijiang  and
      Mittal, Arpit  and
      Schlichtkrull, Michael  and
      Thorne, James  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the Ninth Fact Extraction and {VER}ification Workshop ({FEVER})",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/port-ingestion-scripts/2026.fever-1.10/",
    pages = "127--135",
    ISBN = "979-8-89176-365-4",
    abstract = "This paper presents an efficiency-aware pipeline for automated fact-checking of real-world image{--}text claims that treats multimodality as a controllable design variable rather than a property that must be uniformly propagated through every stage of the system. The approach decomposes claims into verification questions, assigns each to text- or image-related types, and applies modality-aware retrieval strategies, while ultimately relying on text-only evidence for verdict prediction and justification generation. Evaluated on the AVerImaTeC dataset within the FEVER-9 shared task, the system achieves competitive question, evidence, verdict, and justification scores and ranks fourth overall, outperforming the official baseline on evidence recall, verdict accuracy, and justification quality despite not using visual evidence during retrieval. These results demonstrate that strong performance on multimodal fact-checking can be achieved by selectively controlling where visual information influences retrieval and reasoning, rather than performing full multimodal fusion at every stage of the pipeline."
}