3218IR at SemEval-2020 Task 11: Conv1D and Word Embedding in Propaganda Span Identification at News Articles

Dimas Sony Dewantara, Indra Budi, Muhammad Okky Ibrohim


Abstract
In this paper, we present the result of our experiment with a variant of 1 Dimensional Convolutional Neural Network (Conv1D) hyper-parameters value. We describe the system entered by the team of Information Retrieval Lab. Universitas Indonesia (3218IR) in the SemEval 2020 Task 11 Sub Task 1 about propaganda span identification in news articles. The best model obtained an F1 score of 0.24 in the development set and 0.23 in the test set. We show that there is a potential for performance improvement through the use of models with appropriate hyper-parameters. Our system uses a combination of Conv1D and GloVe as Word Embedding to detect propaganda in the fragment text level.
Anthology ID:
2020.semeval-1.225
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:
1716–1721
Language:
URL:
https://aclanthology.org/2020.semeval-1.225
DOI:
10.18653/v1/2020.semeval-1.225
Bibkey:
Cite (ACL):
Dimas Sony Dewantara, Indra Budi, and Muhammad Okky Ibrohim. 2020. 3218IR at SemEval-2020 Task 11: Conv1D and Word Embedding in Propaganda Span Identification at News Articles. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1716–1721, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
3218IR at SemEval-2020 Task 11: Conv1D and Word Embedding in Propaganda Span Identification at News Articles (Dewantara et al., SemEval 2020)
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PDF:
https://aclanthology.org/2020.semeval-1.225.pdf