@inproceedings{da-san-martino-etal-2019-findings,
title = "Findings of the {NLP}4{IF}-2019 Shared Task on Fine-Grained Propaganda Detection",
author = "Da San Martino, Giovanni and
Barr{\'o}n-Cede{\~n}o, Alberto and
Nakov, Preslav",
editor = "Feldman, Anna and
Da San Martino, Giovanni and
Barr{\'o}n-Cede{\~n}o, Alberto and
Brew, Chris and
Leberknight, Chris and
Nakov, Preslav",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5024",
doi = "10.18653/v1/D19-5024",
pages = "162--170",
abstract = "We present the shared task on Fine-Grained Propaganda Detection, which was organized as part of the NLP4IF workshop at EMNLP-IJCNLP 2019. There were two subtasks. FLC is a fragment-level task that asks for the identification of propagandist text fragments in a news article and also for the prediction of the specific propaganda technique used in each such fragment (18-way classification task). SLC is a sentence-level binary classification task asking to detect the sentences that contain propaganda. A total of 12 teams submitted systems for the FLC task, 25 teams did so for the SLC task, and 14 teams eventually submitted a system description paper. For both subtasks, most systems managed to beat the baseline by a sizable margin. The leaderboard and the data from the competition are available at \url{http://propaganda.qcri.org/nlp4if-shared-task/}.",
}
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%0 Conference Proceedings
%T Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection
%A Da San Martino, Giovanni
%A Barrón-Cedeño, Alberto
%A Nakov, Preslav
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Barrón-Cedeño, Alberto
%Y Brew, Chris
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F da-san-martino-etal-2019-findings
%X We present the shared task on Fine-Grained Propaganda Detection, which was organized as part of the NLP4IF workshop at EMNLP-IJCNLP 2019. There were two subtasks. FLC is a fragment-level task that asks for the identification of propagandist text fragments in a news article and also for the prediction of the specific propaganda technique used in each such fragment (18-way classification task). SLC is a sentence-level binary classification task asking to detect the sentences that contain propaganda. A total of 12 teams submitted systems for the FLC task, 25 teams did so for the SLC task, and 14 teams eventually submitted a system description paper. For both subtasks, most systems managed to beat the baseline by a sizable margin. The leaderboard and the data from the competition are available at http://propaganda.qcri.org/nlp4if-shared-task/.
%R 10.18653/v1/D19-5024
%U https://aclanthology.org/D19-5024
%U https://doi.org/10.18653/v1/D19-5024
%P 162-170
Markdown (Informal)
[Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection](https://aclanthology.org/D19-5024) (Da San Martino et al., NLP4IF 2019)
ACL