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2019 EMNLP EMNLP 2019

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

Abstract

AbstractThis paper presents the CUNLP submission for the NLP4IF 2019 shared-task on Fine-Grained 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.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Natural Language Processing

Authors

Tariq Alhindi, Jonas Pfeiffer, Smaranda Muresan

Topics

Artificial Intelligence > Core AI > Interpretability Machine Learning > Core Methods > Classification Natural Language Processing > Applications > Text Classification Machine Learning > Learning Types > Transfer Learning Artificial Intelligence > Core AI > Language

Keywords

transfer learning text classification sentence classification neural model ablation study propaganda detection sentence-level classification fragment-level classification fragment classification
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