BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features based Ensemble Learning

TL;DR

This paper presents a system for detecting propaganda in news articles by combining multi-granularity contextual embeddings and linguistic features within ensemble learning, addressing class imbalance effectively.

Contribution

It introduces a multi-granularity knowledge sharing method for span identification and an ensemble of BERT and logistic regression with linguistic features for technique classification.

Findings

Linguistic features improve minority class detection.

Multi-granularity embeddings enhance span identification.

Ensemble approach achieves competitive results.

Abstract

Propaganda spreads the ideology and beliefs of like-minded people, brainwashing their audiences, and sometimes leading to violence. SemEval 2020 Task-11 aims to design automated systems for news propaganda detection. Task-11 consists of two sub-tasks, namely, Span Identification - given any news article, the system tags those specific fragments which contain at least one propaganda technique; and Technique Classification - correctly classify a given propagandist statement amongst 14 propaganda techniques. For sub-task 1, we use contextual embeddings extracted from pre-trained transformer models to represent the text data at various granularities and propose a multi-granularity knowledge sharing approach. For sub-task 2, we use an ensemble of BERT and logistic regression classifiers with linguistic features. Our results reveal that the linguistic features are the strong indicators for covering minority classes in a highly imbalanced dataset.