This paper describes the SMASH submission to SemEval-2026 Task~9 on multilingual, multicultural, and multi-event polarisation detection. The task comprises (i) binary polarisation detection, (ii) multi-label classification of polarisation types, and (iii) multi-label identification of polarisation manifestations across all available languages. We propose a language-adaptive ensemble framework combining monolingual and multilingual encoder-only transformers, together with a principled out-of-fold (OOF) threshold tuning strategy. Instead of relying on fixed probability thresholds, we jointly tune ensemble weights and class-wise decision thresholds to directly optimise macro-F1 under the official evaluation metric. Our experiments show that (1) monolingual encoders dominate in several high-resource languages but benefit from complementary multilingual signals, (2) no single multilingual backbone universally outperforms others across languages and subtasks, and (3) language-specific class threshold tuning substantially improves performance due to large cross-lingual variation in class distributions. Our results demonstrate that careful logit-level ensembling and threshold tuning provide strong performance for multilingual, imbalanced, multi-label polarisation detection. Across 22 evaluation languages, SMASH ranks among the top three systems in a substantial number of language–subtask pairs. Specifically, it ranks in the top three for 5 languages in Subtask 1, 14 languages in Subtask 2, and 16 languages in Subtask 3, demonstrating strong and consistent performance across diverse languages and tasks. Our system achieves average macro-F1 scores of 0.81, 0.62, and 0.53 for Subtasks 1, 2, and 3, respectively.

Counterspeech generation has emerged as a promising approach to combat online hate speech, with recent work focusing on controlling attributes used in counterspeech, such as strategies or intents. While these attributes are often evaluated automatically using classifiers, a key goal of this evaluation is to compare the performance of different generation models. However, the validity of such evaluation results is questionable when the classifiers themselves have only modest performance. This paper examines the automatic evaluation of counterspeech attributes using a multi-attribute counterspeech dataset containing 2,728 samples. We investigate when automatic evaluation can be trusted for model comparison and address the limitations of current evaluation methodologies. We make concrete recommendations for how to perform classifier validation before model evaluation. Our classifier validation results demonstrate that even limited classifiers can produce trustworthy model rankings. Therefore, we argue that when comparing counterspeech generation models, a classifier’s ability to rank generation models is a more direct measure of its practical utility than traditional classification metrics, e.g., accuracy and F1.