Neural Architectures for Fine-Grained Propaganda Detection in News
This work addresses propaganda detection for news analysis, but it is incremental as it applies existing methods to a specific task.
The paper tackled fine-grained propaganda detection in news at sentence and fragment levels using neural architectures and feature extraction, achieving 3rd and 4th place rankings in the shared task.
This paper describes our system (MIC-CIS) details and results of participation in the fine-grained propaganda detection shared task 2019. To address the tasks of sentence (SLC) and fragment level (FLC) propaganda detection, we explore different neural architectures (e.g., CNN, LSTM-CRF and BERT) and extract linguistic (e.g., part-of-speech, named entity, readability, sentiment, emotion, etc.), layout and topical features. Specifically, we have designed multi-granularity and multi-tasking neural architectures to jointly perform both the sentence and fragment level propaganda detection. Additionally, we investigate different ensemble schemes such as majority-voting, relax-voting, etc. to boost overall system performance. Compared to the other participating systems, our submissions are ranked 3rd and 4th in FLC and SLC tasks, respectively.