论文标题
Reintel：在社交网站上负责信息识别的多模式数据挑战
ReINTEL: A Multimodal Data Challenge for Responsible Information Identification on Social Network Sites
论文作者
论文摘要
本文报告了Reintel共享的任务，以在社交网站上负责信息识别，该任务是在越南语言和语音处理的第七届年度研讨会上举办的（VLSP 2020）。鉴于有各自的文本，视觉内容和元数据的新闻，需要参与者分类新闻是“可靠”还是“不可靠”。为了产生公平的基准，我们介绍了一个新颖的人类通知数据集，该数据集是从越南一个社交网络收集的10,000多个新闻。所有模型将根据AUC-ROC分数进行评估，AUC-ROC得分是分类的典型评估度量。比赛是在Codalab平台上进行的。在两个月内，挑战吸引了60多名参与者，并记录了近1,000个提交条目。
This paper reports on the ReINTEL Shared Task for Responsible Information Identification on social network sites, which is hosted at the seventh annual workshop on Vietnamese Language and Speech Processing (VLSP 2020). Given a piece of news with respective textual, visual content and metadata, participants are required to classify whether the news is `reliable' or `unreliable'. In order to generate a fair benchmark, we introduce a novel human-annotated dataset of over 10,000 news collected from a social network in Vietnam. All models will be evaluated in terms of AUC-ROC score, a typical evaluation metric for classification. The competition was run on the Codalab platform. Within two months, the challenge has attracted over 60 participants and recorded nearly 1,000 submission entries.
