  Verifying fact-checking claims poses a significant challenge, even for
humans. Recent approaches have demonstrated that decomposing claims into
relevant questions to gather evidence enhances the efficiency of the
fact-checking process. In this paper, we provide empirical evidence showing
that this question decomposition can be effectively automated. We demonstrate
that smaller generative models, fine-tuned for the question generation task
using data augmentation from various datasets, outperform large language models
by up to 8%. Surprisingly, in some cases, the evidence retrieved using
machine-generated questions proves to be significantly more effective for
fact-checking than that obtained from human-written questions. We also perform
manual evaluation of the decomposed questions to assess the quality of the
questions generated.
