Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection
4th Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, NLP4IF 2021
; : 82-92, 2021.
Article
in English
| Scopus | ID: covidwho-2046701
ABSTRACT
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. © 2021 Association for Computational Linguistics.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
4th Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, NLP4IF 2021
Year:
2021
Document Type:
Article
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