Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection (2020)

Abstract: 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.

## introduction propaganda is the approach deliberately designed with specific purposes to influence the opinions of readers. different from the fake news which is entirely made-up and has no verifiable facts, propaganda is possibly built upon an element of truth, and conveys information with strong emotion or somewhat biased. this characteristic makes propaganda more effective and unnoticed through the rise of social media platforms. some examples of propagandistic texts and definitions of corresponding techniques are shown in figure 1 . we study the problem of fine-grained propaganda detection in this work, which is possible 1. appeal to fear: seeking to build support for an idea by instilling anxiety. our convention occurs at a moment of crisis for our nation 1 , said trump. "the attacks on our police, and the terrorism in our cities, threaten our very way of life 1 . trump tweeted: "i'm building a wall, ok? i'm building a wall 2 . i am going to do very well with the hispanics, the mexicans." thanks to the recent release of propaganda techniques corpus @xcite . different from earlier works @xcite that mainly study propaganda detection at a coarse-grained level, namely predicting whether a document is propagandistic or not, the fine-grained propaganda detection requires to identify the tokens of particular propaganda techniques in news articles @xcite propose strong baselines in a multi-task learning manner, which are trained by binary detection of propaganda at sentence level and fine-grained propaganda detection over 18 techniques at token level. such data-driven methods have the merits of convenient end-to-end learning and strong generalization, however, they cannot guarantee the consistency between sentence-level and token-level predictions. in addition, it is appealing to integrate human knowledge into data-driven approaches. in this paper, we introduce an approach named latexpro that leverages logical and textual knowledge for propaganda detection. @xcite , we develop a bertbased multi-task learning approach as the base model, which makes predictions for 18 propaganda techniques at both sentence level and token level. based on that, we inject two types of knowledge as additional objectives to regularize the learning process. specifically, we exploit logic knowledge by transforming the consistency between sentencelevel and token-level predictions with propositional boolean expressions. besides, we use the textual definition of propaganda techniques by first representing each of them as a contextual vector and then minimizing the distances to corresponding model parameters in semantic space. we conduct extensive experiments on propaganda techniques corpus (ptc) @xcite , a large manually annotated dataset for fine-grained propaganda detection. results show that our knowledge-augmented method significantly improves a strong multi-task learning approach. in particular, our model greatly improves precision, demonstrating leveraging declarative knowledge expressed in both first-order logic and natural language can help the model to make more accurate predictions. more importantly, further analysis indicates that augmenting the learning process with declarative knowledge reduces the percentage of inconsistency in model predictions. the contributions of this paper are summarized as follows: dataset. this paper utilizes propaganda techniques corpus (ptc) @xcite for experiments. ptc is a manually annotated dataset for fine-grained propaganda detection, containing 293/ 57/ 101 articles and 14,857/ 2,108/ 4,265 corresponding sentences for training, validation and testing, respectively. each article is annotated with the start and end of the propaganda text span as well as the type of propaganda technique. as the annotations of the official testing set are not publicly available, we divided the official validation set into a validation set of 22 articles and a test set of 35 articles. the statistics of all 18 propaganda techniques and their frequencies (instances per technique) are shown as evaluation. for the slc task, we evaluate the models with precision, recall and micro-averaged f 1 scores. as for the flc task, we adopt the evaluation script provided by figure 2: overview of our proposed model. a bert-based multi-task learning approach is adopted to make predictions for 18 propaganda techniques at both sentence level and token level. we introduce two types of knowledge as additional objectives: (1) logical knowledge about the consistency between sentence-level and token-level predictions, and (2) textual knowledge from literal definitions of propaganda techniques. ## method in this section, we present our approach latexpro as shown in figure 2 , which injects declarative knowledge of fine-grained propaganda techniques into neural networks. we first present our base model ( §3.1), which is a multi-task learning framework that slightly extends the model @xcite . afterward, we introduce two ways to regularize the learning process with logical knowledge about the consistency between sentencelevel and token-level predictions ( §3.2) and textual knowledge from literal definitions of propaganda techniques ( §3.3). at last, we describe the training and inference procedures ( §3.4). ## conclusion in this paper, we propose a fine-grained multitask learning approach, which leverages declarative knowledge to detect propaganda techniques in news articles. specifically, the declarative knowledge is expressed in both first-order logic and natural language, which are used as regularizers to obtain better propaganda representations and improve logical consistency between coarse-and finegrained predictions, respectively. extensive experiments on the ptc dataset demonstrate that our knowledge-augmented method achieves superior performance with more consistent between sentence-level and token-level predictions.