Neural Architectures for Fine-Grained Propaganda Detection in News

TL;DR

This paper presents a neural network-based system for fine-grained propaganda detection in news, utilizing multi-granularity architectures and ensemble methods, achieving competitive results in a shared task.

Contribution

It introduces multi-tasking neural architectures combining CNN, LSTM-CRF, and BERT for sentence and fragment level propaganda detection, along with ensemble strategies.

Findings

Ranked 3rd in fragment-level detection

Ranked 4th in sentence-level detection

Effective use of multi-granularity neural models

Abstract

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.