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

TL;DR

This paper presents multilingual and monolingual transformer-based models for classifying news genre, framing, and persuasion techniques in online news across multiple languages, achieving top rankings in SemEval-2023 Task 3.

Contribution

It introduces ensemble and adapter-based transformer models tailored for multilingual news classification, demonstrating state-of-the-art performance across subtasks and languages.

Findings

Ensemble of mBERT models ranked joint-first for German news genre classification.

Monolingual RoBERTa and multilingual XLM-RoBERTa ensembles achieved top ranks in framing detection.

Monolingual RoBERTa-Base and multilingual mBERT models ranked in the top 10 for persuasion technique detection.

Abstract

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.