论文标题
为事实核对复杂主张产生字面和暗示的子问题
Generating Literal and Implied Subquestions to Fact-check Complex Claims
论文作者
论文摘要
验证复杂的政治主张是一项具有挑战性的任务，尤其是当政客使用各种策略巧妙地歪曲事实时。自动核对系统在这里不足，他们的预测在孤立中并不是很有用，因为我们不知道索赔的哪一部分是真实的，哪些不是。在这项工作中，我们专注于将复杂的主张分解成一组全面的Yes-no子问题集，其答案影响了索赔的真实性。我们提出了SoipeDecomp，这是一个用于1000多个索赔的分解数据集。鉴于事实检查者撰写的索赔及其验证段落，我们训练有素的注释者写了一个涵盖了原始主张及其内在方面的明确命题的子问题，例如询问其他政治背景，以改变我们对索赔真实性的看法。我们研究了最新的模型是否可以产生这样的子问题，表明这些模型会产生合理的问题，但可以预测原始索赔中没有证据的全面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确定相关的证据，以检查全面的主张并通过答案获得真实性，这表明它们可以成为事实检查管道的有用部分。
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.
