Neural Architectures for Fine-Grained Propaganda Detection in News
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.
Keywords
Cite
@article{arxiv.1909.06162,
title = {Neural Architectures for Fine-Grained Propaganda Detection in News},
author = {Pankaj Gupta and Khushbu Saxena and Usama Yaseen and Thomas Runkler and Hinrich Schütze},
journal= {arXiv preprint arXiv:1909.06162},
year = {2019}
}
Comments
EMNLP2019: Fine-grained propaganda detection shared task at NLP4IF workshop (EMNLP2019)