@inproceedings{cao-etal-2026-automatic,
title = "The Automatic Verification of Image-Text Claims ({AV}er{I}ma{T}e{C}) Shared Task",
author = "Cao, Rui and
Chen, Yulong and
Deng, Zhenyun and
Schlichtkrull, Michael and
Vlachos, Andreas",
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://aclanthology.org/2026.fever-1.6/",
doi = "10.18653/v1/2026.fever-1.6",
pages = "74--90",
ISBN = "979-8-89176-365-4",
abstract = "The Automatic Verification of Image-Text Claims (AVerImaTeC) shared task aims to advance system development for retrieving evidence and verifying real-world image-text claims. Participants were allowed to either employ external knowledge sources, such as web search engines, or leverage the curated knowledge store provided by the organizers. System performance was evaluated using the AVerImaTeC score, defined as a conditional verdict accuracy in which a verdict is considered correct only when the associated evidence score exceeds a predefined threshold. The shared task attracted 14 submissions during the development phase and 6 submissions during the testing phase. All participating systems in the testing phase outperformed the baseline provided. The winning team, HUMAN, achieved an AVerImaTeC score of 0.5455. This paper provides a detailed description of the shared task, presents the complete evaluation results, and discusses key insights and lessons learned."
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%0 Conference Proceedings
%T The Automatic Verification of Image-Text Claims (AVerImaTeC) Shared Task
%A Cao, Rui
%A Chen, Yulong
%A Deng, Zhenyun
%A Schlichtkrull, Michael
%A Vlachos, Andreas
%Y Akhtar, Mubashara
%Y Aly, Rami
%Y Cao, Rui
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Ninth Fact Extraction and VERification Workshop (FEVER)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-365-4
%F cao-etal-2026-automatic
%X The Automatic Verification of Image-Text Claims (AVerImaTeC) shared task aims to advance system development for retrieving evidence and verifying real-world image-text claims. Participants were allowed to either employ external knowledge sources, such as web search engines, or leverage the curated knowledge store provided by the organizers. System performance was evaluated using the AVerImaTeC score, defined as a conditional verdict accuracy in which a verdict is considered correct only when the associated evidence score exceeds a predefined threshold. The shared task attracted 14 submissions during the development phase and 6 submissions during the testing phase. All participating systems in the testing phase outperformed the baseline provided. The winning team, HUMAN, achieved an AVerImaTeC score of 0.5455. This paper provides a detailed description of the shared task, presents the complete evaluation results, and discusses key insights and lessons learned.
%R 10.18653/v1/2026.fever-1.6
%U https://aclanthology.org/2026.fever-1.6/
%U https://doi.org/10.18653/v1/2026.fever-1.6
%P 74-90
Markdown (Informal)
[The Automatic Verification of Image-Text Claims (AVerImaTeC) Shared Task](https://aclanthology.org/2026.fever-1.6/) (Cao et al., FEVER 2026)
ACL