SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles
This work addresses the challenge of automated propaganda detection in news for researchers and practitioners, but it is incremental as it builds on existing NLP methods and datasets.
The paper tackled the problem of detecting propaganda techniques in news articles by organizing a competition with two subtasks: identifying text fragments containing propaganda and classifying them into 14 techniques, attracting 250 teams and 44 submissions, with the best systems using pre-trained Transformers and ensembles.
We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. The task featured two subtasks. Subtask SI is about Span Identification: given a plain-text document, spot the specific text fragments containing propaganda. Subtask TC is about Technique Classification: given a specific text fragment, in the context of a full document, determine the propaganda technique it uses, choosing from an inventory of 14 possible propaganda techniques. The task attracted a large number of participants: 250 teams signed up to participate and 44 made a submission on the test set. In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For both subtasks, the best systems used pre-trained Transformers and ensembles.