| Abstract | Predicting emotions and emotional reactions during conversations and within texts poses challenges, even for advanced AI systems. The second iteration of the WASSA Empathy and Personality Shared Task focuses on creating innovative models that can anticipate emotional responses to news articles containing harmful content across four tasks. In this paper, we introduce our Fraunhofer SIT team’s solutions for the three tasks: Task 1 (CONVD), Task 2 (CONVT), and Task 3 (EMP). It involves combining LLM-driven data augmentation with fuzzy labels and fine-tuning RoBERTa models pre-trained on sentiment classification tasks to solve the regression problems. In the competition, our solutions achieved 1st place in Track 1 (CONV-dialog), 8th in Track 2 (CONV-turn), and 3rd place in Track 3 (EMP). |
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