2025
ACL
ACL 2025
Pixel Phantoms at SemEval-2025 Task 11: Enhancing Multilingual Emotion Detection with a T5 and mT5-Based Approach
Abstract
AbstractEmotion 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.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Natural Language Processing
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Keyword Pioneer
— emotion intensity estimation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Topics
Artificial Intelligence > Core AI > Multimodal Learning
Natural Language Processing > Understanding > Sentiment Analysis
Natural Language Processing > Resources & Methods > Large Language Models
Natural Language Processing > Resources & Methods > Multilingual NLP
Natural Language Processing > Applications > Sentiment Analysis
Deep Learning > Models > Transformers