Pixel Phantoms at S em E val-2025 Task 11: Enhancing Multilingual Emotion Detection with a T5 and m T 5-Based Approach (2025)

Abstract: Emotion recognition in textual data is a crucial NLP task with applications in sentiment analysis and mental health monitoring. SemEval 2025 Task 11 introduces a multilingual dataset spanning 28 languages, including low-resource ones, to improve cross-lingual emotion detection. Our approach utilizes T5 for English and mT5 for other languages, fine-tuning them for multi-label classification and emotion intensity estimation. Our findings demonstrate the effectiveness of transformer-based models in capturing nuanced emotional expressions across diverse languages.

## introduction emotion recognition in textual data is a crucial task in natural language processing (nlp), with applications in sentiment analysis, mental health monitoring, and human-computer interaction. however, detecting emotions across multiple languages presents significant challenges due to linguistic diversity, cultural differences, and limited resources for many languages. semeval-2025 task 11, bridging the gap in text-based emotion detection, aims to improve emotion detection by providing a multilingual dataset covering 28 languages, including low-resource ones. our approach focuses on track a (multi-label emotion detection) and track b (emotion intensity estimation). for track a, we fine-tune mt5 to classify multiple perceived emotions (joy, sadness, fear, anger, surprise, and disgust) within a given text. the model processes multilingual input and predicts relevant emotions simultaneously. for track b, we extend this model to predict emotion intensities on an ordinal scale, ensuring that the system can accurately gauge varying degrees of emotional expression. this helps capture subtle differences in emotional intensity across languages. during the task, we observed that multilingual emotion detection remains challenging due to variations in emotional expression and imbalanced datasets for low-resource languages. our results show that transformer-based models like t5 and mt5 effectively capture emotional nuances, but performance varies depending on data availability. a key struggle was handling subjective emotional interpretations and linguistic inconsistencies across languages. despite these challenges, our findings highlight the potential of multilingual models in improving cross-lingual emotion recognition. ## related works emotion detection in text has been a long-standing challenge in nlp, evolving from traditional machine learning methods to deep learning and transformer-based models. early approaches relied on feature engineering with statistical models such as svms and naive bayes, leveraging sentiment lexicons and syntactic dependencies @xcite . the rise of deep learning introduced lstms, grus, and cnns, which enhanced contextual understanding @xcite . however, transformer-based architectures like bert, roberta, and t5 revolutionized the field with self-attention mechanisms and large-scale multilingual pretraining, significantly improving multi-label emotion classification @xcite . the growing interest in multilingual emotion detection has led to datasets like brighter @xcite , which provide high-quality human-annotated emotion recognition data across 28 languages. our work builds on these advancements by applying t5 and mt5 models to semeval-2025 task 11, addressing multilingual emotion detection challenges in two tracks: multi-label classification (track a) and emotion intensity estimation @xcite . unlike previous studies that focused primarily on high-resource languages, we fine-tune models on diverse linguistic datasets, leveraging brighter and other multilingual resources. additionally, our approach refines multi-label classification by using separate label schemas for different languages and tasks, setting it apart from standard transformer-based emotion classification models @xcite . our experimental setup ensures robust evaluation across different linguistic contexts, further enhancing emotion understanding in low-resource languages @xcite . these advancements contribute to the broader goal of improving cross-linguistic emotion recognition and fostering more inclusive ai-driven applications in natural language processing. ## conclusion this study demonstrated the effectiveness of transformer-based models, particularly t5 and mt5, for multilingual emotion detection. these models leveraged pre-trained knowledge and fine-tuning to classify emotions across diverse languages, achieving strong results in highresource languages like hindi, marathi, and russian while facing challenges in low-resource languages such as swahili, yoruba, and makhuwa due to data scarcity. the strong performance of spanish, despite not being a high-resource language, suggests that factors like annotation quality and linguistic structure significantly impact model effectiveness. emotion intensity estimation followed similar trends, highlighting the necessity of refined annotations and better training data. future work should focus on enhancing dataset availability, optimizing model fine-tuning, and incorporating external linguistic resources to improve cross-linguistic performance and generalizability. while the study focused on t5 and mt5 due to their unified text-to-text architecture, future work should also explore competitive encoder-only models like xlm-r. additionally, leveraging transfer learning techniques, such as adapter layers or language-specific fine-tuning, could further improve low-resource performance.