CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection

Verena Blaschke, Maxim Korniyenko, Sam Tureski


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)
Venues:
COLING | SemEval
SIGs:
SIGLEX | SIGSEM
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.192.pdf
Code
 cicl-iscl/CyberWallE-propaganda-detection