Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels
This work addresses propaganda detection for NLP applications, but it is incremental as it applies existing methods to a specific shared task without major breakthroughs.
The paper tackled propaganda detection at sentence and fragment levels using fine-tuned neural models, achieving 5th place out of 26 teams on sentence-level and 5th out of 11 on fragment-level classification in a shared task.
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.