@inproceedings{altiti-etal-2020-just,
title = "{JUST} at {S}em{E}val-2020 Task 11: Detecting Propaganda Techniques Using {BERT} Pre-trained Model",
author = "Altiti, Ola and
Abdullah, Malak and
Obiedat, Rasha",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.229",
doi = "10.18653/v1/2020.semeval-1.229",
pages = "1749--1755",
abstract = "This paper presents the submission to semeval-2020 task 11, Detection of Propaganda Techniques in News Articles. Knowing that there are two subtasks in this competition, we have participated in the Technique Classification subtask (TC), which aims to identify the propaganda techniques used in a specific propaganda span. We have used and implemented various models to detect propaganda. Our proposed model is based on BERT uncased pre-trained language model as it has achieved state-of-the-art performance on multiple NLP benchmarks. The performance results of our proposed model have scored 0.55307 F1-Score, which outperforms the baseline model provided by the organizers with 0.2519 F1-Score, and our model is 0.07 away from the best performing team. Compared to other participating systems, our submission is ranked 15th out of 31 participants.",
}
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<abstract>This paper presents the submission to semeval-2020 task 11, Detection of Propaganda Techniques in News Articles. Knowing that there are two subtasks in this competition, we have participated in the Technique Classification subtask (TC), which aims to identify the propaganda techniques used in a specific propaganda span. We have used and implemented various models to detect propaganda. Our proposed model is based on BERT uncased pre-trained language model as it has achieved state-of-the-art performance on multiple NLP benchmarks. The performance results of our proposed model have scored 0.55307 F1-Score, which outperforms the baseline model provided by the organizers with 0.2519 F1-Score, and our model is 0.07 away from the best performing team. Compared to other participating systems, our submission is ranked 15th out of 31 participants.</abstract>
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%0 Conference Proceedings
%T JUST at SemEval-2020 Task 11: Detecting Propaganda Techniques Using BERT Pre-trained Model
%A Altiti, Ola
%A Abdullah, Malak
%A Obiedat, Rasha
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F altiti-etal-2020-just
%X This paper presents the submission to semeval-2020 task 11, Detection of Propaganda Techniques in News Articles. Knowing that there are two subtasks in this competition, we have participated in the Technique Classification subtask (TC), which aims to identify the propaganda techniques used in a specific propaganda span. We have used and implemented various models to detect propaganda. Our proposed model is based on BERT uncased pre-trained language model as it has achieved state-of-the-art performance on multiple NLP benchmarks. The performance results of our proposed model have scored 0.55307 F1-Score, which outperforms the baseline model provided by the organizers with 0.2519 F1-Score, and our model is 0.07 away from the best performing team. Compared to other participating systems, our submission is ranked 15th out of 31 participants.
%R 10.18653/v1/2020.semeval-1.229
%U https://aclanthology.org/2020.semeval-1.229
%U https://doi.org/10.18653/v1/2020.semeval-1.229
%P 1749-1755
Markdown (Informal)
[JUST at SemEval-2020 Task 11: Detecting Propaganda Techniques Using BERT Pre-trained Model](https://aclanthology.org/2020.semeval-1.229) (Altiti et al., SemEval 2020)
ACL