We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. We leverage declarative knowledge expressed in both natural language and first-order logic. The former refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. The latter refers to logical consistency between coarse- and fine- grained predictions, which is used to regularize the training process with propositional Boolean expressions. We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection. Experiments show that our method achieves superior performance, demonstrating that injecting declarative knowledge expressed in both natural language and first-order logic can help the model to make more accurate predictions.

本文介绍了一种注入 fine-grained propaganda 技术的声明式知识的方法，该方法通过利用一阶逻辑和自然语言表达的声明知识进行训练，以提高模型的准确性。通过在粗粒度和细粒度预测之间保持逻辑一致性来规范训练过程，同时利用字面定义每种宣传技术来获得类表示来规范模型参数。实验表明，我们的方法在 fine-grained propaganda 检测上取得了卓越的性能。

利用文本和一阶逻辑中的声明性知识进行细粒度宣传检测