Abstract
This paper describes our participation in the SemEval-2020 task Detection of Propaganda Techniques in News Articles. We participate in both subtasks: Span Identification (SI) and Technique Classification (TC). We use a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask. Our architectures are built using embeddings from BERT in combination with additional lexical features and extensive label post-processing. Our systems achieve a rank of 8 out of 35 teams in the SI subtask (F1-score: 43.86%) and 8 out of 31 teams in the TC subtask (F1-score: 57.37%).- Anthology ID:
- 2020.semeval-1.192
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1469–1480
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.192
- DOI:
- 10.18653/v1/2020.semeval-1.192
- Cite (ACL):
- Verena Blaschke, Maxim Korniyenko, and Sam Tureski. 2020. CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1469–1480, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection (Blaschke et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.semeval-1.192.pdf
- Code
- cicl-iscl/CyberWallE-propaganda-detection