RAVE：检索与评分感知的可验证声明检测
RAVE: Retrieval and Scoring Aware Verifiable Claim Detection
AI总结：
本文提出RAVE框架，通过结合证据检索与相关性及来源可信度信号来检测可验证声明，在CT22-test和PoliClaim-test上均优于文本和检索基线方法。
摘要：
社交媒体上错误信息的快速传播凸显了对可扩展事实核查工具的需求。其中一个关键步骤是声明检测，即识别可以被客观验证的陈述。先前的方法通常依赖语言线索或声明可核查性，但这些方法在模糊的政治话语和推文等多样化格式中表现不佳。我们提出RAVE（检索与评分感知的可验证声明检测），这是一个将证据检索与相关性和来源可信度的结构化信号相结合的框架。在CT22-test和PoliClaim-test上的实验表明，RAVE在准确率和F1分数上均一致优于仅文本和基于检索的基线方法。
英文摘要：
The rapid spread of misinformation on social media underscores the need for scalable fact-checking tools. A key step is claim detection, which identifies statements that can be objectively verified. Prior approaches often rely on linguistic cues or claim check-worthiness, but these struggle with vague political discourse and diverse formats such as tweets. We present RAVE (Retrieval and Scoring Aware Verifiable Claim Detection), a framework that combines evidence retrieval with structured signals of relevance and source credibility. Experiments on CT22-test and PoliClaim-test show that RAVE consistently outperforms text-only and retrieval-based baselines in both accuracy and F1.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
