论文标题
事实或小说：验证科学主张
Fact or Fiction: Verifying Scientific Claims
论文作者
论文摘要
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，并证明与对Wikipedia或政治新闻训练的模型相比，简单的域适应技术大大提高了性能。我们表明，我们的系统能够通过识别绳索-19语料库的证据来验证与COVID-19的主张。我们的实验表明，Scifact将为开发旨在检索包含专业领域知识的语料库的新系统的开发提供具有挑战性的测试床。此新任务的数据和代码可在https://github.com/allenai/scifact上公开获取。 https://scifact.apps.allenai.org可以找到排行榜和COVID-19的事实检查演示。
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 simple domain adaptation techniques substantially improve performance compared to models trained on Wikipedia or political news. We show that our system is able to verify claims related to COVID-19 by identifying evidence from the CORD-19 corpus. Our experiments indicate that SciFact will provide a challenging testbed for the development of new systems designed to retrieve and reason over corpora containing specialized domain knowledge. Data and code for this new task are publicly available at https://github.com/allenai/scifact. A leaderboard and COVID-19 fact-checking demo are available at https://scifact.apps.allenai.org.
