S olomon at S em E val-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles (2020)

Abstract: This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 ”Detection of Propaganda Techniques in News Articles”. We participated in Task ”Technique Classification” (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependent-minority-class classifiers. A special classifier, which employs dynamically adapted Least Common Sub-sequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.

## introduction in today's digital world, information is being shared more rapidly across boundaries owing to the rampant use of social media platforms. while these platforms provide a forum for people to exchange their views with ease, they also serve as a medium for spreading misinformation in the absence of proper quality control mechanisms. two paradigms of misinformation are often reported, fake news and propaganda. while fake news corresponds to factually incorrect information, propaganda is more nuanced. it aims at advancing a specific agenda using psychological and rhetorical techniques (e.g use of biased or loaded language, repetition etc) and may not be always factually wrong. prior efforts have been made to segregate propagandist content @xcite , @xcite , @xcite from non-propagandist. these methods use distant supervision based techniques to obtain document level labels, often labelling all articles from a propagandist news outlet as propaganda. however, such techniques introduce inevitable noise @xcite . to this end, @xcite have introduced a propaganda dataset where they label propaganda techniques at fragment level, thus providing finer control and explainability. for example, following is an instance of propaganda with type "slogan". t rump tweeted, " make america great again ! ## conclusion in this paper, we examine capability of the transformer based pre-trained language model, roberta. we illustrated that fine-tuned roberta performed better on minority classes compared to bert. we also introduced novel approach of handling the minority classes using an ensemble of one-vs-one simple classifiers. furthermore, we handle the repetition class separately using dynamic lcs algorithm. experiments show the improvement in f1 score when fine-tuned roberta predictions are augmented with minority class classifiers and repetition classifier.