  For the WASSA 2024 Empathy and Personality Prediction Shared Task, we propose
a novel turn-level empathy detection method that decomposes empathy into six
psychological indicators: Emotional Language, Perspective-Taking, Sympathy and
Compassion, Extroversion, Openness, and Agreeableness. A pipeline of text
enrichment using a Large Language Model (LLM) followed by DeBERTA fine-tuning
demonstrates a significant improvement in the Pearson Correlation Coefficient
and F1 scores for empathy detection, highlighting the effectiveness of our
approach. Our system officially ranked 7th at the CONV-turn track.
