论文标题
授权事实检查器！在Twitter上自动识别索赔跨度
Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter
论文作者
论文摘要
在Covid-19之后，医疗和政治主张的广泛扩散导致错误信息和虚假新闻的大量增加。当前的Vogue是雇用手动事实检查器，以有效地对此类数据进行分类和验证，以打击这种声称缠绕的错误信息的雪崩。但是，信息传播的速度使得它极大地超过了事实检查者的优势。因此，为了帮助手动事实检查器消除多余的内容，必须自动识别和提取帖子中存在的索赔（MIS）信息的片段。在这项工作中，我们介绍了索赔跨度识别（CSI）的新任务。我们提出了Curt，这是一个大规模的Twitter语料库，具有令牌级别的索赔跨度超过7.5k的推文。此外，与标准令牌分类基线一起，我们用基于适配器的罗伯塔（Roberta）变体Daberta对数据集进行了基准测试。实验结果证明，达伯塔（Daberta）的表现优于几个评估指标的基线系统，提高了约1.5分。我们还报告了详细的错误分析，以验证模型的性能以及消融研究。最后，我们发布了公共用途的全面跨度注释指南。
The widespread diffusion of medical and political claims in the wake of COVID-19 has led to a voluminous rise in misinformation and fake news. The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation. However, the rate of information dissemination is such that it vastly outpaces the fact-checkers' strength. Therefore, to aid manual fact-checkers in eliminating the superfluous content, it becomes imperative to automatically identify and extract the snippets of claim-worthy (mis)information present in a post. In this work, we introduce the novel task of Claim Span Identification (CSI). We propose CURT, a large-scale Twitter corpus with token-level claim spans on more than 7.5k tweets. Furthermore, along with the standard token classification baselines, we benchmark our dataset with DABERTa, an adapter-based variation of RoBERTa. The experimental results attest that DABERTa outperforms the baseline systems across several evaluation metrics, improving by about 1.5 points. We also report detailed error analysis to validate the model's performance along with the ablation studies. Lastly, we release our comprehensive span annotation guidelines for public use.
