Explainable Automated Fact-Checking for Public Health Claims
Explainable Automated Fact-Checking for Public Health Claims
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DOI：
10.18653/v1/2020.emnlp-main.623
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Neema Kotonya;Francesca Toni
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文献类型：
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作者：
Neema Kotonya;Francesca Toni
事实核查的任务是根据可信的证据评估索赔的说法，以核实索赔的真实性。绝大多数事实核查研究都只关注政治主张。很少有研究探讨其他主题的事实核查，特别是需要专门知识的主题。我们提出了第一个研究的可解释的事实核查索赔，需要特定的专业知识。在我们的案例研究中，我们选择了公共卫生的背景。为了支持这个案例研究，我们构建了一个新的数据集PUBHEALTH，其中包含11.8K的索赔，并附有记者精心制作的黄金标准解释（即，判断），以支持索赔的事实核查标签。我们探索两个任务：准确性预测和解释生成。我们还定义和评估，与人类和计算，解释质量的三个一致性属性。我们的研究结果表明，通过对域内数据进行训练，可以在需要特定专业知识的索赔的可解释的自动事实检查方面取得进展。
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.