Fact or Fiction: Verifying Scientific Claims
Fact or Fiction: Verifying Scientific Claims
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DOI：
10.18653/v1/2020.emnlp-main.609
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
David Wadden;Kyle Lo;Lucy Lu Wang;Shanchuan Lin;Madeleine van Zuylen;Arman Cohan;Hannaneh Hajishirzi
中科院分区：
文献类型：
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作者：
David Wadden;Kyle Lo;Lucy Lu Wang;Shanchuan Lin;Madeleine van Zuylen;Arman Cohan;Hannaneh Hajishirzi
我们介绍了科学主张验证，这是一项新任务，是从研究文献中选择摘要的一项新任务，其中包含支持或驳斥给定科学主张的证据，并确定为每个决定辩护的理由。为了研究这项任务，我们构建了Scifact，这是一个由1.4K专家写的科学主张的数据集，并与标签和理由注释的循证摘要配对。我们开发了用于Scifact的基线模型，并证明这些模型受益于有关Wikipedia文章的大量主张的合并培训以及新的Scifact数据。我们表明，我们的索赔验证系统能够确定与19 /36在Cord-19 Corpus上的Covid-19有关的23 /36索赔的合理证据。我们的结果和实验强烈表明，我们的新任务和数据将支持重大的未来研究工作。
We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that supports or refutes a given scientific claim, and to identify rationales justifying each decision. To study this task, we construct SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. We develop baseline models for SciFact, and demonstrate that these models benefit from combined training on a large dataset of claims about Wikipedia articles, together with the new SciFact data. We show that our claim verification system is able to identify plausible evidence for 23 / 36 claims relevant to COVID-19 on the CORD-19 corpus. Our results and experiments strongly suggest that our new task and data will support significant future research efforts.