Team UBD at SemEval-2025 Task 11: Balancing Class and Task Importance for Emotion Detection

AbstractThis article presents the systems used by Team UBD in Task 11 of SemEval-2025. We participated in all three sub-tasks, namely Emotion Detection, Emotion Intensity Estimation and Cross-Lingual Emotion Detection. In our solutions we make use of publicly available Language Models (LMs) already fine-tuned for the Emotion Detection task, as well as open-sourced models for Neural Machine Translation (NMT). We robustly adapt the existing LMs to the new data distribution, balance the importance of all emotions and classes and also use a custom sampling scheme.We present fine-grained results in all sub-tasks and analyze multiple possible sources for errors for the Cross-Lingual Emotion Detection sub-task.