  This paper describes our approach for SemEval-2023 Task 3: Detecting the
category, the framing, and the persuasion techniques in online news in a
multi-lingual setup. For Subtask 1 (News Genre), we propose an ensemble of
fully 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 all
the languages, by using two separate ensembles: a monolingual
RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task
adaptive pretraining. For Subtask 3 (Persuasion Techniques), we train a
monolingual RoBERTa-Base model for English and a multilingual mBERT model for
the remaining languages, which achieved top 10 for all languages, including 2nd
for English. For each subtask, we compared monolingual and multilingual
approaches, and considered class imbalance techniques.
