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Detecting Fake News Conspiracies with Multitask and Prompt-Based Learning
MediaEval 2021 Workshop, MediaEval 2021 ; 3181, 2021.
Article in English | Scopus | ID: covidwho-2011296
ABSTRACT
In this paper, we present our participation to the MediaEval-2021 challenge on fake news detection about coronavirus related Tweets. It consists in three subtasks that can be seen as multi-labels classification problems we solved with transformer-based models. We show that each task can be solved independantly with mutiple monotasks models or jointly with an unique multitasks model. Moreover, we propose a prompt-based model that has been finetuned to generate classifications from a pre-trained model based on DistilGPT-2. Our experimental results show the multitask model to be the best to solve the three tasks. Copyright 2021 for this paper by its authors.
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Collection: Databases of international organizations Database: Scopus Language: English Journal: MediaEval 2021 Workshop, MediaEval 2021 Year: 2021 Document Type: Article

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Collection: Databases of international organizations Database: Scopus Language: English Journal: MediaEval 2021 Workshop, MediaEval 2021 Year: 2021 Document Type: Article