@inproceedings{paraschiv-etal-2020-upb,
title = "{UPB} at {S}em{E}val-2020 Task 11: Propaganda Detection with Domain-Specific Trained {BERT}",
author = "Paraschiv, Andrei and
Cercel, Dumitru-Clementin and
Dascalu, Mihai",
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-1.244",
doi = "10.18653/v1/2020.semeval-1.244",
pages = "1853--1857",
abstract = "Manipulative and misleading news have become a commodity for some online news outlets and these news have gained a significant impact on the global mindset of people. Propaganda is a frequently employed manipulation method having as goal to influence readers by spreading ideas meant to distort or manipulate their opinions. This paper describes our participation in the SemEval-2020, Task 11: Detection of PropagandaTechniques in News Articles competition. Our approach considers specializing a pre-trained BERT model on propagandistic and hyperpartisan news articles, enabling it to create more adequate representations for the two subtasks, namely propaganda Span Identification (SI) and propaganda Technique Classification (TC). Our proposed system achieved a F1-score of 46.060{\%} in subtask SI, ranking 5th in the leaderboard from 36 teams and a micro-averaged F1 score of 54.302{\%} for subtask TC, ranking 19th from 32 teams.",
}
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%0 Conference Proceedings
%T UPB at SemEval-2020 Task 11: Propaganda Detection with Domain-Specific Trained BERT
%A Paraschiv, Andrei
%A Cercel, Dumitru-Clementin
%A Dascalu, Mihai
%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 paraschiv-etal-2020-upb
%X Manipulative and misleading news have become a commodity for some online news outlets and these news have gained a significant impact on the global mindset of people. Propaganda is a frequently employed manipulation method having as goal to influence readers by spreading ideas meant to distort or manipulate their opinions. This paper describes our participation in the SemEval-2020, Task 11: Detection of PropagandaTechniques in News Articles competition. Our approach considers specializing a pre-trained BERT model on propagandistic and hyperpartisan news articles, enabling it to create more adequate representations for the two subtasks, namely propaganda Span Identification (SI) and propaganda Technique Classification (TC). Our proposed system achieved a F1-score of 46.060% in subtask SI, ranking 5th in the leaderboard from 36 teams and a micro-averaged F1 score of 54.302% for subtask TC, ranking 19th from 32 teams.
%R 10.18653/v1/2020.semeval-1.244
%U https://aclanthology.org/2020.semeval-1.244
%U https://doi.org/10.18653/v1/2020.semeval-1.244
%P 1853-1857
Markdown (Informal)
[UPB at SemEval-2020 Task 11: Propaganda Detection with Domain-Specific Trained BERT](https://aclanthology.org/2020.semeval-1.244) (Paraschiv et al., SemEval 2020)
ACL