Turn-Level Empathy Prediction Using Psychological Indicators
This work addresses empathy prediction in conversational AI for applications like mental health support, but it is incremental as it builds on existing shared task frameworks.
The paper tackled turn-level empathy detection by decomposing empathy into six psychological indicators and using an LLM text enrichment pipeline with DeBERTA fine-tuning, achieving a 7th-place ranking in the CONV-turn track with improved Pearson Correlation Coefficient and F1 scores.
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.