Inno at SemEval-2020 Task 11: Leveraging Pure Transformer for Multi-Class Propaganda Detection
This work addresses the challenge of multi-class propaganda detection for natural language processing applications, presenting an incremental improvement in a specific competition task.
The paper tackled the problem of detecting 18 propaganda techniques in news articles by using a pure Transformer-based model, achieving an overall F1 score of 0.6 on the validation set and 0.58 on the test set with non-zero scores for all classes.
The paper presents the solution of team "Inno" to a SEMEVAL 2020 task 11 "Detection of propaganda techniques in news articles". The goal of the second subtask is to classify textual segments that correspond to one of the 18 given propaganda techniques in news articles dataset. We tested a pure Transformer-based model with an optimized learning scheme on the ability to distinguish propaganda techniques between each other. Our model showed 0.6 and 0.58 overall F1 score on validation set and test set accordingly and non-zero F1 score on each class on both sets.