Neural Architectures for Fine-Grained Propaganda Detection in News

Pankaj Gupta, Khushbu Saxena, Usama Yaseen, Thomas Runkler, Hinrich Schütze


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.
Anthology ID:
D19-5012
Volume:
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | NLP4IF | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
92–97
Language:
URL:
https://aclanthology.org/D19-5012
DOI:
10.18653/v1/D19-5012
Bibkey:
Cite (ACL):
Pankaj Gupta, Khushbu Saxena, Usama Yaseen, Thomas Runkler, and Hinrich Schütze. 2019. Neural Architectures for Fine-Grained Propaganda Detection in News. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 92–97, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Neural Architectures for Fine-Grained Propaganda Detection in News (Gupta et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5012.pdf