CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection

TL;DR

This paper presents an ensemble approach with feature engineering for propaganda detection in news articles, achieving competitive rankings in SemEval-2020 tasks using BERT embeddings and lexical features.

Contribution

The paper introduces a novel ensemble model with feature engineering for propaganda detection, combining BERT embeddings and lexical features for improved performance.

Findings

Achieved 8th place in Span Identification subtask (F1: 43.86%)

Achieved 8th place in Technique Classification subtask (F1: 57.37%)

Demonstrated effectiveness of feature engineering with BERT in ensemble models.

Abstract

This paper describes our participation in the SemEval-2020 task Detection of Propaganda Techniques in News Articles. We participate in both subtasks: Span Identification (SI) and Technique Classification (TC). We use a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask. Our architectures are built using embeddings from BERT in combination with additional lexical features and extensive label post-processing. Our systems achieve a rank of 8 out of 35 teams in the SI subtask (F1-score: 43.86%) and 8 out of 31 teams in the TC subtask (F1-score: 57.37%).