YNU-HPCC at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Using Multiple Prediction Headers

AbstractThis paper describes the our team’s participation in Subtask A of Task 11 at SemEval-2025, focusing on multilingual text-based emotion classification. The team employed the RoBERTa model, enhanced with modifications to the output head to allow independent prediction of six emotions: anger, disgust, fear, joy, sadness, and surprise. The dataset was translated into English using Google Translate to facilitate processing. The study found that a single prediction head outperformed simultaneous prediction of multiple emotions, and training on the translated dataset yielded better results than using the original dataset. The team incorporated Focal Loss and R-Drop techniques to address class imbalance and improve model stability. Future work will continue to explore improvements in this area.