TY - GEN
T1 - Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection
AU - Shaar, Shaden
AU - Alam, Firoj
AU - Da San Martino, Giovanni
AU - Nikolov, Alex
AU - Zaghouani, Wajdi
AU - Nakov, Preslav
AU - Feldman, Anna
N1 - Publisher Copyright:
© 2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that tweet contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking; also, whether it is harmful to society, and whether it requires the attention of policy makers. Task 2 focused on censorship detection, and was offered in Chinese. A total of ten teams submitted systems for task 1, and one team participated in task 2; nine teams also submitted a system description paper. Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used. Most submissions achieved sizable improvements over several baselines, and the best systems used pre-trained Transformers and ensembles. The data, the scorers and the leader-boards for the tasks are available at http://gitlab.com/NLP4IF/nlp4if-2021.
AB - We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that tweet contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking; also, whether it is harmful to society, and whether it requires the attention of policy makers. Task 2 focused on censorship detection, and was offered in Chinese. A total of ten teams submitted systems for task 1, and one team participated in task 2; nine teams also submitted a system description paper. Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used. Most submissions achieved sizable improvements over several baselines, and the best systems used pre-trained Transformers and ensembles. The data, the scorers and the leader-boards for the tasks are available at http://gitlab.com/NLP4IF/nlp4if-2021.
UR - http://www.scopus.com/inward/record.url?scp=85137978234&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85137978234
T3 - NLP4IF 2021 - NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, Proceedings of the 4th Workshop
SP - 82
EP - 92
BT - NLP4IF 2021 - NLP for Internet Freedom
A2 - Feldman, Anna
A2 - Da San Martino, Giovanni
A2 - Leberknight, Chris
A2 - Nakov, Preslav
PB - Association for Computational Linguistics (ACL)
T2 - 4th Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, NLP4IF 2021
Y2 - 6 June 2021
ER -