  Fact-checking is essential due to the explosion of misinformation in the
media ecosystem. Although false information exists in every language and
country, most research to solve the problem mainly concentrated on huge
communities like English and Chinese. Low-resource languages like Vietnamese
are necessary to explore corpora and models for fact verification. To bridge
this gap, we construct ViWikiFC, the first manual annotated open-domain corpus
for Vietnamese Wikipedia Fact Checking more than 20K claims generated by
converting evidence sentences extracted from Wikipedia articles. We analyze our
corpus through many linguistic aspects, from the new dependency rate, the new
n-gram rate, and the new word rate. We conducted various experiments for
Vietnamese fact-checking, including evidence retrieval and verdict prediction.
BM25 and InfoXLM (Large) achieved the best results in two tasks, with BM25
achieving an accuracy of 88.30% for SUPPORTS, 86.93% for REFUTES, and only
56.67% for the NEI label in the evidence retrieval task, InfoXLM (Large)
achieved an F1 score of 86.51%. Furthermore, we also conducted a pipeline
approach, which only achieved a strict accuracy of 67.00% when using InfoXLM
(Large) and BM25. These results demonstrate that our dataset is challenging for
the Vietnamese language model in fact-checking tasks.
