Claim detection and verification are crucial for news understanding and have emerged as promising technologies for mitigating misinformation in news. However, most existing work focus on analysis of claim sentences while overlooking crucial background attributes, such as the claimer, claim objects, and other knowledge connected to the claim. In this work, we present NewsClaims , a new benchmark for knowledge-aware claim detection in the news domain. We re-define the claim detection problem to include extraction of additional background attributes related to the claim and release 529 claims annotated over 103 news articles. In addition, NewsClaims aims to benchmark claim detection systems in emerging scenarios, comprising unseen topics with little or no training data. Finally, we provide a comprehensive evaluation of various zero-shot and prompt-based baselines for this new benchmark.
翻译：发现和核实索赔要求对于了解新闻至关重要,并已成为减少新闻错误的有希望的技术,然而,大多数现有工作的重点是分析索赔判决,同时忽略关键的背景特征,例如索赔者、索赔对象和其他与索赔有关的知识。在这项工作中,我们提出新闻索赔,这是在新闻领域探测知识意识索赔要求的新基准。我们重新研究索赔调查问题,以包括提取与索赔要求有关的其他背景属性,并发表超过103篇新闻文章,附加说明的529项索赔要求。此外,《新闻索赔要求》旨在为新情况中的索赔探测系统制定基准,其中包括很少或根本没有培训数据的隐蔽主题。最后,我们全面评价这一新基准的各种零点和即时基准。
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