Turn-Level Empathy Prediction Using Psychological Indicators

TL;DR

This paper introduces a novel turn-level empathy detection method that decomposes empathy into six psychological indicators, using LLM-based text enrichment and DeBERTA fine-tuning, achieving improved performance in empathy prediction.

Contribution

It presents a new approach for empathy detection that combines psychological indicators with LLM-based text enrichment and fine-tuning, advancing the state-of-the-art.

Findings

Significant improvement in Pearson Correlation Coefficient

Enhanced F1 scores for empathy detection

Ranked 7th at the CONV-turn track

Abstract

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.