构建真实图文事实核查数据集，支持网络证据推理。
AVerImaTeC: A Dataset for Automatic Verification of Image-Text Claims with Evidence from the Web
- 基于1297个真实图文主张，标注网页证据QA对
- 通过时序约束与双重验证提升证据充分性
- 适合研究图文事实核查与开放网络证据检索
文本主张常配以图像增强可信度并在社交媒体传播，但也加剧了虚假信息扩散风险。现有图文事实自动核查数据集多为合成数据，缺乏反映判断逻辑的证据标注。本文提出AVerImaTeC，包含1,297个真实世界图文主张，每条均配有来自网络的问答对形式证据，体现判别过程的分解推理。通过主张归一化、时序约束证据标注及两阶段充分性检验，缓解事实核查数据集中常见的语境依赖、时间泄露和证据不足问题。通过标注者间一致性评估，判别结果κ值达0.742，问答对一致率为74.7%。此外，提出新型证据检索评估方法，并开展广泛实验，建立使用开放网络证据验证图文主张的基线性能。
原文摘要 · Abstract (English)
Textual claims are often accompanied by images to enhance their credibility and spread on social media, but this also raises concerns about the spread of misinformation. Existing datasets for automated verification of image-text claims remain limited, as they often consist of synthetic claims and lack evidence annotations to capture the reasoning behind the verdict. In this work, we introduce AVerImaTeC, a dataset consisting of 1,297 real-world image-text claims. Each claim is annotated with question-answer (QA) pairs containing evidence from the web, reflecting a decomposed reasoning regarding the verdict. We mitigate common challenges in fact-checking datasets such as contextual dependence, temporal leakage, and evidence insufficiency, via claim normalization, temporally constrained evidence annotation, and a two-stage sufficiency check. We assess the consistency of the annotation in AVerImaTeC via inter-annotator studies, achieving a $κ=0.742$ on verdicts and $74.7\%$ consistency on QA pairs. We also propose a novel evaluation method for evidence retrieval and conduct extensive experiments to establish baselines for verifying image-text claims using open-web evidence.
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