Abstract
This 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.- Anthology ID:
- D19-5013
- 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
- Editors:
- Anna Feldman, Giovanni Da San Martino, Alberto Barrón-Cedeño, Chris Brew, Chris Leberknight, Preslav Nakov
- Venue:
- NLP4IF
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 98–102
- Language:
- URL:
- https://aclanthology.org/D19-5013
- DOI:
- 10.18653/v1/D19-5013
- Cite (ACL):
- Tariq Alhindi, Jonas Pfeiffer, and Smaranda Muresan. 2019. Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 98–102, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels (Alhindi et al., NLP4IF 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-5013.pdf