@inproceedings{tariq-kementchedjhieva-2026-reveal,
    title = "{REVEAL}: Retrieval-Enhanced Verification for Multimodal Fact-Checking",
    author = "Tariq, Amina  and
      Kementchedjhieva, Yova",
    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/ingest-ccl/2026.fever-1.8/",
    pages = "108--113",
    ISBN = "979-8-89176-365-4",
    abstract = "Multimodal misinformation combines images and text to amplify false narratives, yet most fact-checking research addresses only textualclaims. The AVerImaTeC shared task introduces real-world image-text claims requiring sophisticated evidence retrieval. We present REVEAL (Retrieval-Enhanced Verification with Evidence Accumulation Loop), a system designed to overcome the ``semantic gap,'' defined as the disconnect between the neutral phrasing of claims and the adversarial vocabulary of debunking evidence. Unlike static baselines, REVEAL breaks down the verification task into an iterative context loop, integrating sparse and dense retrieval signals to aggressively target refuting evidence. We achieve a Verdict Accuracy of 23.6{\%} and an Evidence Recall of 27.7{\%} on the test set. Our results outperform the official baseline across all metrics, validating our hybrid retrieval strategy for complex multimodal verification."
}