论文标题
检查概述！ 2020年：自动识别和验证社交媒体中的主张
Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media
论文作者
论文摘要
我们介绍了第三版CheckThat！在2020年CLEF的实验室。该实验室有两种不同语言的五项任务：英语和阿拉伯语。前四个任务构成了社交媒体中的索赔验证的完整管道：任务1关于检查估算的任务1，关于检索以前事实检查的索赔的任务2，关于检索证据的任务3以及索赔验证的任务4。实验室完成了任务5关于政治辩论和演讲中值得支票的估算。总共有67支球队参加了该实验室（从2019年CLEF的47个），其中23个实际上提交了跑步（比Clef 2019中为14次）。大多数团队使用基于BERT，LSTMS或CNN的深层神经网络，并在所有任务上都取得了相当大的改进。在这里，我们描述了任务设置，评估结果以及参与者使用的方法的摘要，并讨论了一些经验教训。最后但并非最不重要的一点是，我们向研究社区释放了实验室的所有数据集以及评估脚本，这应该在校验值得估算和自动索赔验证的重要任务中进行进一步的研究。
We present an overview of the third edition of the CheckThat! Lab at CLEF 2020. The lab featured five tasks in two different languages: English and Arabic. The first four tasks compose the full pipeline of claim verification in social media: Task 1 on check-worthiness estimation, Task 2 on retrieving previously fact-checked claims, Task 3 on evidence retrieval, and Task 4 on claim verification. The lab is completed with Task 5 on check-worthiness estimation in political debates and speeches. A total of 67 teams registered to participate in the lab (up from 47 at CLEF 2019), and 23 of them actually submitted runs (compared to 14 at CLEF 2019). Most teams used deep neural networks based on BERT, LSTMs, or CNNs, and achieved sizable improvements over the baselines on all tasks. Here we describe the tasks setup, the evaluation results, and a summary of the approaches used by the participants, and we discuss some lessons learned. Last but not least, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in the important tasks of check-worthiness estimation and automatic claim verification.
