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.

本研究解决了在新主题下检测值得核查声明的困难，提出了一种领域适应框架来提升阿拉伯语中的核查声明检测能力。我们引入了逐渐主题学习（GTL）模型和基于相似性的逐渐主题学习（SGTL）模型，实验结果表明，所提模型在14个主题中的11个主题上表现优于现有技术，具有显著的性能提升潜力。

支持跨主题的自动化事实核查：基于相似性的逐渐主题学习用于声明检测