In this work, we introduce X-FACT: the largest publicly available
multilingual dataset for factual verification of naturally existing real-world
claims. The dataset contains short statements in 25 languages and is labeled
for veracity by expert fact-checkers. The dataset includes a multilingual
evaluation benchmark that measures both out-of-domain generalization, and
zero-shot capabilities of the multilingual models. Using state-of-the-art
multilingual transformer-based models, we develop several automated
fact-checking models that, along with textual claims, make use of additional
metadata and evidence from news stories retrieved using a search engine.
Empirically, our best model attains an F-score of around 40%, suggesting that
our dataset is a challenging benchmark for evaluation of multilingual
fact-checking models.

本研究介绍了 X-FACT 数据集，这是目前最大的公开的多语言事实验证数据集，其中包含 25 种语言的短语句，并由专业事实检查者标记真实性，数据集包含多语言评估基准测试，并使用现代化的多语言变压器模型开发了多个自动事实检查模型，实证表明模型的最佳性能 F1 分数约为 40％，因此是评估多语言事实检查模型的挑战性基准测试。