Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels

TL;DR

This paper introduces neural models for detecting propaganda at sentence and fragment levels, achieving top-5 rankings in a shared task and analyzing various propaganda techniques.

Contribution

The paper presents fine-tuned neural models for propaganda detection and provides an extensive analysis of their performance across multiple propaganda techniques.

Findings

Achieved 5th place in sentence-level classification

Achieved 5th place in fragment-level classification

Conducted ablation studies and detailed performance analysis

Abstract

This paper presents the CUNLP submission for the NLP4IF 2019 shared-task on FineGrained Propaganda Detection. Our system finished 5th out of 26 teams on the sentence-level classification task and 5th out of 11 teams on the fragment-level classification task based on our scores on the blind test set. We present our models, a discussion of our ablation studies and experiments, and an analysis of our performance on all eighteen propaganda techniques present in the corpus of the shared task.