NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification
Ilya Dimov | Vladislav Korzun | Ivan Smurov
Proceedings of the Fourteenth Workshop on Semantic Evaluation
This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6% and a micro-averaged F-score of 58.2% for those tasks accordingly.