让AI逐步学会识别新话题中的关键待查声明。
Supporting Automated Fact-checking across Topics: Similarity-driven Gradual Topic Learning for Claim Detection
- 分阶段学习新话题，逐步聚焦关键声明
- 14个话题中11个表现优于现有模型
- 适合应对突发新闻事件的自动核查
从海量在线信息中筛选出值得核查的声明，是加速事实核查的关键。然而，当面对此前未见的新话题时，这一任务难度显著提升。本文针对阿拉伯语场景，提出一种领域自适应框架，用于跨话题的事实核查声明检测。我们设计了渐进式话题学习（GTL）模型，通过多阶段训练逐步掌握目标话题下的关键声明特征。进一步提出相似性驱动的渐进式话题学习（SGTL），融合相似度策略增强模型对目标话题的适应能力。实验表明，所提模型在14个话题中的11个上超越当前最优基线，整体性能持续提升。
原文摘要 · Abstract (English)
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.
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