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 this https URL

CFEVER 是一个中文数据集，用于事实提取和验证，利用中文维基百科的内容手动创建了 30,012 个声明，并标记为 “支持”、“反驳” 或 “信息不足”，同时提供详细的证据句子。这个数据集通过 Fleiss' kappa 值（0.7934）的五向评判员一致性达到了新的高度，通过与现有方法和基准测试的实验表明，CFEVER 是一个严格的事实提取和验证基准，可用于开发自动化系统减少人工事实核查工作量。