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Turn-Level Empathy Prediction Using Psychological Indicators

  • arXiv (Cornell University)
  • Cornell University
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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.

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Publication details

DOI
10.48550/arxiv.2407.08607
OpenAlex
W4400611985
Document type
preprint
Language
EN
Source
arXiv (Cornell University)
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