  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. Specifically, we leverage the declarative knowledge
expressed in both first-order logic and natural language. The former refers to
the logical consistency between coarse- and fine-grained predictions, which is
used to regularize the training process with propositional Boolean expressions.
The latter refers to the literal definition of each propaganda technique, which
is utilized to get class representations for regularizing the model parameters.
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 leveraging
declarative knowledge can help the model to make more accurate predictions.
