In this paper, we describe our systems in which the objective is to determine whether a given news article could be considered as hyperpartisan. Hyperpartisan news is news that takes an extremely polarized political standpoint with an intention of creating political divide among the public. We attempted several approaches, including n-grams, sentiment analysis, as well as sentence and document representation using pre-tained ELMo. Our best system using pre-trained ELMo with Bidirectional LSTM achieved an accuracy of 83% through 10-fold cross-validation without much hyperparameter tuning.

翻译：本文描述了我们的系统，其目标是判断给定新闻文章是否可被视为超党派新闻。超党派新闻指采取极端极化政治立场、意图在公众中制造政治分裂的新闻。我们尝试了多种方法，包括n-gram模型、情感分析，以及使用预训练ELMo的句子和文档表示方法。我们最佳的系统采用预训练ELMo与双向LSTM结合，在未进行过多超参数调整的情况下，通过10折交叉验证达到了83%的准确率。