论文标题
多标签宣传检测的大型语言模型
Large Language Models for Multi-label Propaganda Detection
论文作者
论文摘要
在过去的几年中，宣传通过互联网的传播急剧增加。最近，由于它对社会的负面影响，宣传发现已经开始变得重要。在这项工作中，我们描述了WANLP 2022共享任务的方法，该任务在多标签环境中处理宣传检测任务。该任务要求模型将给定文本标记为具有一种或多种类型的宣传技术。共有21种宣传技术要检测到。我们表明，五个型号的合奏在任务上表现最好，而Micro-F1得分为59.73％。我们还进行全面的消融，并为这项工作提出各种未来的方向。
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.
