We present CFEVER, a Chinese dataset designed for Fact Extraction and VERification. CFEVER comprises 30,012 manually created claims based on content in Chinese Wikipedia. Each claim in CFEVER is labeled as "Supports", "Refutes", or "Not Enough Info" to depict its degree of factualness. Similar to the FEVER dataset, claims in the "Supports" and "Refutes" categories are also annotated with corresponding evidence sentences sourced from single or multiple pages in Chinese Wikipedia. Our labeled dataset holds a Fleiss' kappa value of 0.7934 for five-way inter-annotator agreement. In addition, through the experiments with the state-of-the-art approaches developed on the FEVER dataset and a simple baseline for CFEVER, we demonstrate that our dataset is a new rigorous benchmark for factual extraction and verification, which can be further used for developing automated systems to alleviate human fact-checking efforts. CFEVER is available at https://ikmlab.github.io/CFEVER.

翻译：我们提出了CFEVER，一个面向事实抽取与验证的中文数据集。该数据集包含基于中文维基百科内容人工构建的30,012条声明，每条声明被标注为"支持"、"反驳"或"信息不足"以描述其事实性程度。与FEVER数据集类似，"支持"和"反驳"类别的声明还附带了源自中文维基百科单个或多个页面的对应证据句子。该标注数据集在五方标注者间一致性评估中取得了0.7934的Fleiss' kappa值。此外，通过采用基于FEVER数据集开发的最先进方法以及针对CFEVER设计的简单基线进行实验，我们证明该数据集是事实抽取与验证领域新的严格基准，可进一步用于开发自动化系统以减轻人工事实核查工作量。CFEVER数据集可通过https://ikmlab.github.io/CFEVER获取。