UNED at SemEval-2026 Task 9: Sentiment-Aware Transformer Models with Back-Translation Augmentation for Online polarisation Detection
Year: 2026
Type: conference-paper
Abstract: This paper describes our submission to SemEval-2026 Task 9 (Subtask 1) on Spanish online polarisation detection.We investigate whether sentiment-adapted pretrained language models provide an advantage over general-purpose multilingual models for binary polarisation classification.Under a controlled training setup, we compare a base XLM-RoBERTa model, an emotion-adapted model, and a sentiment-adapted XLM-R model trained on Twitter data.To mitigate overfitting in the relatively small training dataset, we additionally apply back-translation as a data augmentation strategy.Experimental results show that the sentiment-adapted checkpoint consistently outperforms the alternative pretrained models under identical conditions.When combined with back-translation augmentation, the final system achieves a macro-averaged F1 score of 0.743 on the preliminary competition leaderboard.These findings suggest that prior adaptation to affective signals in social media can provide beneficial inductive bias for polarisation detection.
Institution: Universidad Nacional de Educación a Distancia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Agencia Estatal de Investigación
OpenAlex ID: W7166860540