Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection
By Giovanni Da San Martino, Alberto Barrón-Cedeño, Preslav Nakov. 2019
90 citations recorded by the source corpus.
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/.
Subfields: Artificial Intelligence, Sociology and Political Science, Natural Language Processing, AI Ethics, Fairness & Society
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Preslav Nakov
- Giovanni Da San Martino
- Alberto Barrón-Cedeño
- Henning Wachsmuth
- Rostislav Petrov
- Todor Mihaylov
- Alessandro Sperduti
- Anette Frank
In Sociology and Political Science
In Natural Language Processing
- Alberto Barrón-Cedeño
- Preslav Nakov
- Henning Wachsmuth
- Giovanni Da San Martino
- Todor Mihaylov
- Yonatan Belinkov
- Anette Frank
In AI Ethics, Fairness & Society
Citations in the corpus
- SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles (2020, methodology): “As detailed in the overview paper of Da San Martino et al. (2019a), the best-performing systems in the shared task used BERT-based contextual representations.”