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Automated fact-checking has emerged as a safeguard against the spread of false information. Existing fact-checking approaches aim to determine whether a news claim is true or false, and they have achieved decent accuracy of veracity prediction. However, ...

Verifying the accuracy of information is a constant task as the prevalence of misinformation on the Web. In this paper, we focus on Chinese fact-checking (CHEF dataset) [1] and improve the performance through prompt-based learning in both evidence ...

Real-world fact verification task aims to verify the factuality of a claim by retrieving evidence from the source document. The quality of the retrieved evidence plays an important role in claim verification. Ideally, the retrieved evidence should be ...

We study the fact-checking problem, which aims to identify the veracity of a given claim. Specifically, we focus on the task of Fact Extraction and VERification (FEVER) and its accompanied dataset. The task consists of the subtasks of retrieving the ...

We extend evidence-aware claim verification to the context of positive-unlabeled (PU) learning. Existing works assume the truth and the falsity of the claims are known for training and form the task as a supervised learning problem. However, this ...

Fact checking is the task of determining if a given claim holds. Several algorithms have been developed to check claims with reference information in the form of facts in a knowledge base. While individual algorithms have been experimentally evaluated ...

Deciding whether a claim is true or false often requires understanding the evidence supporting and contradicting the claim. However, when learning about a controversial claim, human biases and viewpoints may affect which evidence documents are ...