用5个W问题自动验证新闻真假，准确率提升近35%。
Overview of Factify5WQA: Fact Verification through 5W Question-Answering
- 基于5W问答框架对比声明与证据，实现事实核查
- 最佳模型准确率达69.56%，比基线高35%
- 适合关注虚假信息检测与自动化推理的研究者
研究人员发现，假新闻传播速度远超真实新闻，尤其在社交媒体成为年轻人主要新闻来源的背景下，这一问题愈发严重。事实核查因此成为关键任务，众多媒体机构也积极参与。然而，人工核查工作量大、效率低。为推动自动化假新闻检测研究，Factify5WQA共享任务提供了基于5W问答的事实验证数据集。每个声明及其支持文档均关联一组5W问题，用于对比信息源。评估采用BLEU分数衡量答案准确性，再结合分类准确率进行整体评价。参赛方案包括自定义训练和预训练语言模型等方法。最佳团队模型达到69.56%准确率，较基线提升近35%。
原文摘要 · Abstract (English)
Researchers have found that fake news spreads much times faster than real news. This is a major problem, especially in today's world where social media is the key source of news for many among the younger population. Fact verification, thus, becomes an important task and many media sites contribute to the cause. Manual fact verification is a tedious task, given the volume of fake news online. The Factify5WQA shared task aims to increase research towards automated fake news detection by providing a dataset with an aspect-based question answering based fact verification method. Each claim and its supporting document is associated with 5W questions that help compare the two information sources. The objective performance measure in the task is done by comparing answers using BLEU score to measure the accuracy of the answers, followed by an accuracy measure of the classification. The task had submissions using custom training setup and pre-trained language-models among others. The best performing team posted an accuracy of 69.56%, which is a near 35% improvement over the baseline.
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