This paper introduces the Cross-lingual Fact Extraction and VERification
(XFEVER) dataset designed for benchmarking the fact verification models across
different languages. We constructed it by translating the claim and evidence
texts of the Fact Extraction and VERification (FEVER) dataset into six
languages. The training and development sets were translated using machine
translation, whereas the test set includes texts translated by professional
translators and machine-translated texts. Using the XFEVER dataset, two
cross-lingual fact verification scenarios, zero-shot learning and
translate-train learning, are defined, and baseline models for each scenario
are also proposed in this paper. Experimental results show that the
multilingual language model can be used to build fact verification models in
different languages efficiently. However, the performance varies by language
and is somewhat inferior to the English case. We also found that we can
effectively mitigate model miscalibration by considering the prediction
similarity between the English and target languages. The XFEVER dataset, code,
and model checkpoints are available at
this https URL

通过将 Fact Extraction and Verification (FEVER) 数据集的索赔和证据文本翻译成六种语言，我们构建了用于比较不同语言上事实验证模型的跨语言事实提取和验证（XFEVER）数据集。使用 XFEVER 数据集，本文定义了两种跨语言事实验证场景，即零样本学习和翻译训练学习，并提出了每个场景的基准模型。实验结果显示，多语言语言模型可以有效地构建不同语言上的事实验证模型，但性能因语言而异，相对英文情况稍差。我们还发现，通过考虑英语和目标语言之间的预测相似性，可以有效地减轻模型的误差校准问题。XFEVER 数据集、代码和模型检查点可在此 https URL 中获取。