UB _ T el- U at S em E val-2025 Task 11: Emotions Without Borders - A Unified Framework for Multilingual Classification Using Augmentation and Ensemble (2025)

Abstract: In this SemEval 2025 Task 11 paper, we tackled three tracks: Multi-label Emotion Detection, Emotion Intensity, and Cross-lingual Emotion Detection. Our approach harnesses diverse external corpora and robust data augmentation techniques across Spanish, English, and Arabic, enhancing both the diversity and resilience of the dataset. Instead of developing separate models for each language, we merge the data into a unified multilingual dataset, enabling our model to learn cross-lingual patterns and relationships simultaneously. Our ensemble architecture integrates the multilingual strengths of XLM-RoBERTa, a zero-shot classification capability via LLaMA 3, and a specialized pretrained model fine-tuned on English emotion classification. Notably, our system achieved strong performance, ranking 13th for Afrikaans (afr) in Track A, 13th for Amharic (amh) in Track B, and 4th for Hindi (hin) in Track C.

## introduction this paper presents ub_tel-u's submission to semeval 2025 task 11, addressing three tracks: multi-label emotion detection, emotion intensity, and cross-lingual emotion detection @xcite . while emotion recognition plays a critical role in various nlp applications, existing research has predominantly focused on highresource languages, leaving a significant performance gap for low-resource languages that often lack sufficient annotated data @xcite . the shared task provides a large multilingual dataset @xcite , offering an opportunity to explore scalable and cross-lingual emotion detection systems. ## conclusion in this study, we presented a unified multilingual framework for emotion classification, evaluated across three diverse tracks: multi-label classification, emotion intensity regression, and crosslingual generalization. our experimental results show that combining multiple transformer-based models with strategic data preprocessing, augmentation, and ensemble learning substantially enhances system performance. among these components, the ensemble approach proved particularly effective, consistently outperforming individual models by leveraging their complementary strengths. this integration improved robustness across languages and highlighted the importance of model diversity and data enrichment for multilingual emotion recognition. despite these advancements, several challenges remain. emotion intensity prediction continues to exhibit variability across languages, and performance in low-resource settings is still limited by data scarcity and linguistic diversity. to address these issues, future research could explore more sophisticated data augmentation strategies such as back-translation across related languages, generative paraphrasing, or adversarial training. finally, incorporating domain-adaptive pretraining or language-specific adapters could further refine model sensitivity to cultural and linguistic nuances.