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.
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.
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We leverage
Specifically, we leverage the
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natural language and
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and natural language
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literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. The latter refers to
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fine- grained
fine-grained
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The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters.
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injecting declarative knowledge expressed in both natural language and first-order logic
leveraging declarative knowledge