COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic
COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic
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COVID-Fact：关于 COVID-19 大流行的现实世界声明的事实提取和验证
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
S. Muresan
中科院分区：
文献类型：
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作者：
Arkadiy Saakyan;Tuhin Chakrabarty;S. Muresan
我们介绍了关于19009年大流行的4,086个主张的发烧样数据集互联事实。数据集包含索赔，索赔证据以及证据驳斥的矛盾索赔。与以前的方法不同，我们会自动检测到真实的主张及其源文章，然后使用自动方法生成反寻求，而不是使用人类注释。与我们的构建资源一起，我们正式介绍了确定索赔的相关证据的任务，并验证证据是驳斥还是支持给定的索赔。除了科学主张外，我们的数据还包含媒体来源的简化一般主张，使其更适合检测有关Covid-19的一般错误信息。我们的实验表明，Covid Fact将为开发新系统提供一个具有挑战性的测试台，我们的方法将降低构建特定领域特定数据集以检测错误信息的成本。
We introduce a FEVER-like dataset COVID-Fact of 4,086 claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claims using automatic methods rather than employing human annotators. Along with our constructed resource, we formally present the task of identifying relevant evidence for the claims and verifying whether the evidence refutes or supports a given claim. In addition to scientific claims, our data contains simplified general claims from media sources, making it better suited for detecting general misinformation regarding COVID-19. Our experiments indicate that COVID-Fact will provide a challenging testbed for the development of new systems and our approach will reduce the costs of building domain-specific datasets for detecting misinformation.
DOI：
10.18653/v1/k19-1046
发表时间：
2019-08
期刊：
ArXiv
影响因子：
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作者：
Andreas Hanselowski;Christian Stab;Claudia Schulz;Zile Li;Iryna Gurevych
通讯作者：
Andreas Hanselowski;Christian Stab;Claudia Schulz;Zile Li;Iryna Gurevych