论文标题
可解释公共卫生索赔的可自动化事实检查
Explainable Automated Fact-Checking for Public Health Claims
论文作者
论文摘要
事实核对是通过评估其对可靠证据的主张来验证索赔真实性的任务。绝大多数事实核对研究仅着眼于政治主张。很少的研究探讨了其他主题的事实检查，特别是需要专业知识的主题。我们介绍了需要特定专业知识的主张的可解释事实检查的首次研究。对于我们的案例研究，我们选择公共卫生的设置。为了支持此案例研究，我们构建了一个11.8K索赔的新数据集PubHealth，并由记者精心制作，黄金标准解释（即判决），以支持索赔的事实检查标签。我们探索两个任务：真实性预测和解释产生。我们还通过人类和计算来定义和评估解释质量的三个连贯性能。我们的结果表明，通过对内域数据进行培训，可以通过可解释的，自动化的事实来核对需要特定专业知识的索赔。
Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard explanations (i.e., judgments) to support the fact-check labels for claims. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that, by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.
