Verifying complex political claims is a challenging task, especially when politicians use various tactics to subtly misrepresent the facts. Automatic fact-checking systems fall short here, and their predictions like "half-true" are not very useful in isolation, since we have no idea which parts of the claim are true and which are not. In this work, we focus on decomposing a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. We present ClaimDecomp, a dataset of decompositions for over 1000 claims. Given a claim and its verification paragraph written by fact-checkers, our trained annotators write subquestions covering both explicit propositions of the original claim and its implicit facets, such as asking about additional political context that changes our view of the claim's veracity. We study whether state-of-the-art models can generate such subquestions, showing that these models generate reasonable questions to ask, but predicting the comprehensive set of subquestions from the original claim without evidence remains challenging. We further show that these subquestions can help identify relevant evidence to fact-check the full claim and derive the veracity through their answers, suggesting that they can be useful pieces of a fact-checking pipeline.

翻译：核实复杂的政治主张是一项具有挑战性的任务,特别是当政客们使用各种策略来歪曲事实时。 自动的核实事实系统在这里不尽如人意,他们的“半真”预测在孤立的状态下并不十分有用,因为我们不知道索赔的哪些部分是真实的,哪些不是真实的。在这项工作中,我们的重点是将复杂的索赔分为一套全面的“是”的子问题,其答案影响索赔的真实性。我们提出了索赔Decomp,这是1000多件索赔的一组分解数据。鉴于由事实检查员编写的索赔及其核查段落,我们受过训练的警告员写了一些子问题,既包括最初索赔的明确主张,也包括其隐含的方面,例如询问哪些额外的政治背景可以改变我们对索赔的真实性的看法。我们研究的是,现在的模型是否能够产生这样的子问题,表明这些模型会产生合理的问题,但是在没有证据的情况下预测最初索赔的一整套子问题仍然具有挑战性。我们进一步表明,这些子问题可以帮助查明有关证据,以便核实事实的完整主张,并通过其答案来得出真实性。