@inproceedings{da-san-martino-etal-2020-semeval,
title = "{S}em{E}val-2020 Task 11: Detection of Propaganda Techniques in News Articles",
author = "Da San Martino, Giovanni and
Barr{\'o}n-Cede{\~n}o, Alberto and
Wachsmuth, Henning and
Petrov, Rostislav and
Nakov, Preslav",
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-1.186",
doi = "10.18653/v1/2020.semeval-1.186",
pages = "1377--1414",
abstract = "We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. The task featured two subtasks. Subtask SI is about Span Identification: given a plain-text document, spot the specific text fragments containing propaganda. Subtask TC is about Technique Classification: given a specific text fragment, in the context of a full document, determine the propaganda technique it uses, choosing from an inventory of 14 possible propaganda techniques. The task attracted a large number of participants: 250 teams signed up to participate and 44 made a submission on the test set. In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For both subtasks, the best systems used pre-trained Transformers and ensembles.",
}
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<abstract>We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. The task featured two subtasks. Subtask SI is about Span Identification: given a plain-text document, spot the specific text fragments containing propaganda. Subtask TC is about Technique Classification: given a specific text fragment, in the context of a full document, determine the propaganda technique it uses, choosing from an inventory of 14 possible propaganda techniques. The task attracted a large number of participants: 250 teams signed up to participate and 44 made a submission on the test set. In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For both subtasks, the best systems used pre-trained Transformers and ensembles.</abstract>
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%0 Conference Proceedings
%T SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles
%A Da San Martino, Giovanni
%A Barrón-Cedeño, Alberto
%A Wachsmuth, Henning
%A Petrov, Rostislav
%A Nakov, Preslav
%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 da-san-martino-etal-2020-semeval
%X We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. The task featured two subtasks. Subtask SI is about Span Identification: given a plain-text document, spot the specific text fragments containing propaganda. Subtask TC is about Technique Classification: given a specific text fragment, in the context of a full document, determine the propaganda technique it uses, choosing from an inventory of 14 possible propaganda techniques. The task attracted a large number of participants: 250 teams signed up to participate and 44 made a submission on the test set. In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For both subtasks, the best systems used pre-trained Transformers and ensembles.
%R 10.18653/v1/2020.semeval-1.186
%U https://aclanthology.org/2020.semeval-1.186
%U https://doi.org/10.18653/v1/2020.semeval-1.186
%P 1377-1414
Markdown (Informal)
[SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles](https://aclanthology.org/2020.semeval-1.186) (Da San Martino et al., SemEval 2020)
ACL