NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification

Ilya Dimov, Vladislav Korzun, Ivan Smurov


Abstract
This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6% and a micro-averaged F-score of 58.2% for those tasks accordingly.
Anthology ID:
2020.semeval-1.194
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:
1488–1494
Language:
URL:
https://aclanthology.org/2020.semeval-1.194
DOI:
10.18653/v1/2020.semeval-1.194
Bibkey:
Cite (ACL):
Ilya Dimov, Vladislav Korzun, and Ivan Smurov. 2020. NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1488–1494, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification (Dimov et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.194.pdf
Code
 hawkeoni/Semeval2020_task11
Data
MovieNet