Propaganda is a rhetorical technique designed to serve a specific topic, which is often used purposefully in news article to achieve our intended purpose because of its specific psychological effect. Therefore, it is significant to be clear where and what propaganda techniques are used in the news for people to understand its theme efficiently during our daily lives. Recently, some relevant researches are proposed for propaganda detection but unsatisfactorily. As a result, detection of propaganda techniques in news articles is badly in need of research. In this paper, we are going to introduce our systems for detection of propaganda techniques in news articles, which is split into two tasks, Span Identification and Technique Classification. For these two tasks, we design a system based on the popular pretrained BERT model, respectively. Furthermore, we adopt the over-sampling and EDA strategies, propose a sentence-level feature concatenating method in our systems. Experiments on the dataset of about 550 news articles offered by SEMEVAL show that our systems perform state-of-the-art.

宣传是一种为特定主题服务的修辞手法，由于其特定的心理作用，经常在新闻报道中有目的地使用它来达到我们的预期目的。因此，重要的是要弄清楚新闻中在何处使用了什么宣传技术，以便人们在我们的日常生活中有效地理解新闻的主题。近来，有人提出了一些有关侦查宣传的研究，但并不令人满意。结果，迫切需要研究新闻报道中的宣传技术。在本文中，我们将介绍我们的新闻报道中的宣传技术检测系统，该系统分为两个任务，跨度识别和技术分类。对于这两个任务，我们分别基于流行的预训练BERT模型设计了一个系统。此外，我们采用了过采样和EDA策略，在我们的系统中提出了一种句子级特征级联方法。SEMEVAL提供的大约550条新闻文章的数据集上的实验表明，我们的系统执行最新技术。