This is the source code for SheffieldVeraAI at SemEval-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification (task, paper).
Authors are members of the GATE team of the University of Sheffield Natural Language Processing group.
Our models performed well across the board, achieving the highest performance in some languages, including zero-shot languages, and the highest mean across languages for sub-tasks 1 and 2.
Download the organiser data and extract the data dir into ./data/, i.e. each language's data should be at ./data/{LANG}/.
The code to train each subtask is split in the st1, st2 and st3 directories.
This work has been co-funded by the European Union under the Horizon Europe vera.ai (grant 101070093) and Vigilant (grant 101073921) projects and the UK’s innovation agency (InnovateUK) grants 10039055 and 10039039.