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。

XFEVER：跨语言事实验证研究

XFEVER: Exploring Fact Verification across Languages

Fact verification models have enjoyed a fast advancement in the last two
years with the development of pre-trained language models like BERT and the
release of large scale datasets such as FEVER. However, the challenging problem
of fake news detection has not benefited from the improvement of fact
verification models, which is closely related to fake news detection. In this
paper, we propose a simple yet effective approach to connect the dots between
fact verification and fake news detection. Our approach first employs a text
summarization model pre-trained on news corpora to summarize the long news
article into a short claim. Then we use a fact verification model pre-trained
on the FEVER dataset to detect whether the input news article is real or fake.
Our approach makes use of the recent success of fact verification models and
enables zero-shot fake news detection, alleviating the need of large-scale
training data to train fake news detection models. Experimental results on
FakenewsNet, a benchmark dataset for fake news detection, demonstrate the
effectiveness of our proposed approach.

本研究提出了一种简单而有效的方法，在利用先前已经训练好了的模型的基础上，通过使用文本摘要模型将长新闻文章缩短成一个短主张来连接事实验证和假新闻检测，并通过检测更轻松实现零研究用数据的假新闻检测。在 FakenewsNet 数据集上进行的实验结果表明，本研究提出的方法具有很好的效果。