@inproceedings{garcia-sanabria-etal-2026-uned,
title = "{UNED} at {S}em{E}val-2026 Task 9: Sentiment-Aware Transformer Models with Back-Translation Augmentation for Online polarisation Detection",
author = "Garcia Sanabria, Victor and
Rodrigo, Alvaro and
Centeno, Roberto",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.176/",
pages = "1366--1371",
ISBN = "979-8-89176-414-9",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T UNED at SemEval-2026 Task 9: Sentiment-Aware Transformer Models with Back-Translation Augmentation for Online polarisation Detection
%A Garcia Sanabria, Victor
%A Rodrigo, Alvaro
%A Centeno, Roberto
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F garcia-sanabria-etal-2026-uned
%X 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.
%U https://aclanthology.org/2026.semeval-1.176/
%P 1366-1371
Markdown (Informal)
[UNED at SemEval-2026 Task 9: Sentiment-Aware Transformer Models with Back-Translation Augmentation for Online polarisation Detection](https://aclanthology.org/2026.semeval-1.176/) (Garcia Sanabria et al., SemEval 2026)
ACL