CFEVER: A Chinese Fact Extraction and VERification Dataset
Ying-Jia Lin Chun-Yi Lin Chia-Jen Yeh Yi-Ting Li Yun-Yu Hu Chih-Hao Hsu Mei-Feng Lee Hung-Yu Kao
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.