Transformers at S em E val-2020 Task 11: Propaganda Fragment Detection Using Diversified BERT Architectures Based Ensemble Learning (2020)

Abstract: In this paper, we present our approach for the ’Detection of Propaganda Techniques in News Articles’ task as a part of the 2020 edition of International Workshop on Semantic Evaluation. The specific objective of this task is to identify and extract the text segments in which propaganda techniques are used. We propose a multi-system deep learning framework that can be used to identify the presence of propaganda fragments in a news article and also deep dive into the diverse enhancements of BERT architecture which are part of the final solution. Our proposed final model gave an F1-score of 0.48 on the test dataset.

## introduction propaganda (prop-uh-gan-duh) is defined as "information, ideas, opinions, or images, often only giving one part of an argument, that are broadcast, published, or in some other way spread with the intention of influencing people's opinions". developing artificial intelligence to identify the propaganda in an article will not only help the readers but also the authors to be careful and write with more rationality, devoid of unintentional propagandist elements. for this, the paper presents a comprehensive solution by team 'transformers' to detect propaganda spans in news articles. we model this task as a word-level binary classification problem, which needs a lot of latent space and deep representations to encapsulate and detect the words with propaganda engraved in them. the central idea we utilized is fine-tuning a bert @xcite model with multi-sample dropout and convolution layers on top of it to stabilize the training loss and enhance the model generalizability. we have also experimented with our model performance with various loss functions including the focus loss and weighted cross-entropy. finally, we have developed an ensemble of bert based models with combinations of convolutional neural network (cnn) and multi-sample dropout to efficiently detect the text spans having hidden propaganda. our ensemble learning approach 1 has shown to perform exceedingly well and we were one of the top 10 teams in the propaganda span identification challenge . ## background there were two challenge tracks as part of the semeval2020 shared-task 11 @xcite namely, span identification (si) and technique classification (tc). we have worked on the si task and explained our methodology in this paper. the input data provided by the organizers @xcite are news articles in plain text format. the article can be further divided into sentences using new line elements. the dataset has 371 train articles, 75 development articles, and 90 test articles. each article is provided with indices of the start and end of a span of characters that represent a propaganda span. the details of the data provided can be found in