The spread of propaganda through the internet has increased drastically over the past years. Lately, propaganda detection has started gaining importance because of the negative impact it has on society. In this work, we describe our approach for the WANLP 2022 shared task which handles the task of propaganda detection in a multi-label setting. The task demands the model to label the given text as having one or more types of propaganda techniques. There are a total of 21 propaganda techniques to be detected. We show that an ensemble of five models performs the best on the task, scoring a micro-F1 score of 59.73%. We also conduct comprehensive ablations and propose various future directions for this work.
翻译:过去几年来,通过互联网宣传的传播急剧增加,最近,由于宣传对社会的负面影响,宣传探测开始变得日益重要。在这项工作中,我们描述了我们对WANLP 2022共同任务的方法,该方法在多标签环境下处理宣传探测任务。这项任务要求该模型将特定文本标为具有一种或多种宣传技术。总共21种宣传技术有待检测。我们显示,五种模型的组合在这项工作中表现最佳,获得59.73%的微F1分。我们还进行全面的布局,为这项工作提出各种未来方向。