Varifocal Question Generation for Fact-checking
Nedjma Ousidhoum, Zhangdie Yuan, Andreas Vlachos

TL;DR
This paper introduces Varifocal, a question generation method for fact-checking that uses different focal points within a claim and its metadata to produce more relevant questions, improving automatic and manual evaluation results.
Contribution
The paper presents Varifocal, a novel question generation approach leveraging focal points within claims and metadata, advancing fact-checking question generation.
Findings
Outperforms previous methods on fact-checking question datasets
Generates more relevant and informative questions according to manual evaluation
Demonstrates effectiveness in generating clarification questions for product descriptions
Abstract
Fact-checking requires retrieving evidence related to a claim under investigation. The task can be formulated as question generation based on a claim, followed by question answering. However, recent question generation approaches assume that the answer is known and typically contained in a passage given as input, whereas such passages are what is being sought when verifying a claim. In this paper, we present {\it Varifocal}, a method that generates questions based on different focal points within a given claim, i.e.\ different spans of the claim and its metadata, such as its source and date. Our method outperforms previous work on a fact-checking question generation dataset on a wide range of automatic evaluation metrics. These results are corroborated by our manual evaluation, which indicates that our method generates more relevant and informative questions. We further demonstrate the…
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