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This paper describes our approach for SemEval-2023 Task 3: Detecting thecategory, the framing, and the persuasion techniques in online news in amulti-lingual setup. For Subtask 1 (News Genre), we propose an ensemble offully trained and adapter mBERT models which was ranked joint-first for German,and had the highest mean rank of multi-language teams. For Subtask 2 (Framing),we achieved first place in 3 languages, and the best average rank across allthe languages, by using two separate ensembles: a monolingualRoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and taskadaptive pretraining. For Subtask 3 (Persuasion Techniques), we train amonolingual RoBERTa-Base model for English and a multilingual mBERT model forthe remaining languages, which achieved top 10 for all languages, including 2ndfor English. For each subtask, we compared monolingual and multilingualapproaches, and considered class imbalance techniques.

SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification