支持跨主题自动化事实核查：基于相似性的渐进式主题学习用于候选声明检测
Supporting Automated Fact-checking across Topics: Similarity-driven Gradual Topic Learning for Claim Detection
AI总结：
针对跨主题的阿拉伯语候选声明检测，提出渐进式主题学习（GTL）及其基于相似性的变体（SGTL），在14个主题中的11个上优于最先进基线。
摘要：
选择值得核查的声明（check-worthy claims）进行事实核查，被视为通过从海量在线声明中筛选并排序值得核查的声明以加速事实核查过程的关键环节。然而，当模型需要处理与先前所见主题不同的新主题时，候选声明检测任务变得更加具有挑战性。在本研究中，我们针对阿拉伯语提出了一种跨主题的候选声明检测领域自适应框架，以采纳新主题，模拟全球每日涌现事件的真实场景。我们提出了渐进式主题学习（Gradual Topic Learning, GTL）模型，该模型构建了逐步学习的能力，并在学习过程的多个阶段中强调目标主题的值得核查声明。此外，我们引入了基于相似性的渐进式主题学习（Similarity-driven Gradual Topic Learning, SGTL）模型，该模型将渐进式学习与针对目标主题的基于相似性的策略相结合。我们的实验证明了所提出模型的有效性，显示出在所研究的14个主题中，有11个主题上整体性能优于最先进的基线模型。
英文摘要：
Selecting check-worthy claims for fact-checking is considered a crucial part of expediting the fact-checking process by filtering out and ranking the check-worthy claims for being validated among the impressive amount of claims could be found online. The check-worthy claim detection task, however, becomes more challenging when the model needs to deal with new topics that differ from those seen earlier. In this study, we propose a domain-adaptation framework for check-worthy claims detection across topics for the Arabic language to adopt a new topic, mimicking a real-life scenario of the daily emergence of events worldwide. We propose the Gradual Topic Learning (GTL) model, which builds an ability to learning gradually and emphasizes the check-worthy claims for the target topic during several stages of the learning process. In addition, we introduce the Similarity-driven Gradual Topic Learning (SGTL) model that synthesizes gradual learning with a similarity-based strategy for the target topic. Our experiments demonstrate the effectiveness of our proposed model, showing an overall tendency for improving performance over the state-of-the-art baseline across 11 out of the 14 topics under study.
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。
