Abstract
Since propaganda became more common technique in news, it is very important to look for possibilities of its automatic detection. In this paper, we present neural model architecture submitted to the SemEval-2020 Task 11 competition: “Detection of Propaganda Techniques in News Articles”. We participated in both subtasks, propaganda span identification and propaganda technique classification. Our model utilizes recurrent Bi-LSTM layers with pre-trained word representations and also takes advantage of self-attention mechanism. Our model managed to achieve score 0.405 F1 for subtask 1 and 0.553 F1 for subtask 2 on test set resulting in 17th and 16th place in subtask 1 and subtask 2, respectively.- Anthology ID:
- 2020.semeval-1.232
- 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:
- 1771–1778
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.232
- DOI:
- 10.18653/v1/2020.semeval-1.232
- Cite (ACL):
- Matej Martinkovic, Samuel Pecar, and Marian Simko. 2020. NLFIIT at SemEval-2020 Task 11: Neural Network Architectures for Detection of Propaganda Techniques in News Articles. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1771–1778, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- NLFIIT at SemEval-2020 Task 11: Neural Network Architectures for Detection of Propaganda Techniques in News Articles (Martinkovic et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.semeval-1.232.pdf