We describe the system submitted by the Jack Ryder team to SemEval-2019 Task 4 on Hyperpartisan News Detection. The task asked participants to predict whether a given article is hyperpartisan, i.e., extreme-left or extreme-right. We propose an approach based on BERT with fine-tuning, which was ranked 7th out 28 teams on the distantly supervised dataset, where all articles from a hyperpartisanon-hyperpartisan news outlet are considered to be hyperpartisanon-hyperpartisan. On a manually annotated test dataset, where human an-notators double-checked the labels, we were ranked 29th out of 42 teams.
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机译：我们描述了Jack Ryder团队提交给SemEval-2019 Task 4 on Hyperpartisan News Detection的系统。任务要求参与者预测给定的文章是党派倾向，即极端左派还是极端右派。我们提出了一种基于BERT的微调方法，该方法在远程监督数据集中的28个团队中排名第7，其中来自超级党派/非超级党派新闻媒体的所有文章均被视为超级党派/非超级党派。在人工注释的测试数据集上，人类注释者仔细检查了标签，在42个团队中，我们排名第29位。
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