Turn-Level Empathy Prediction Using Psychological Indicators
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 11 Jul 2024.
Builds on
9 of 20 references.
References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
- 2018From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbotscited by 85
- 2018Modeling Empathy and Distress in Reaction to News Storiescited by 52
- 2022Empathic Conversations: A Multi-level Dataset of Contextualized Conversationscited by 20
- 2021EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Predictioncited by 17
- 2021Modeling User Empathy Elicited by a Robot Storytellercited by 7
- 2018A Multimodal LSTM for Predicting Listener Empathic Responses Over Timecited by 5
- 2020Automated Empathy Detection for Oncology Encounterscited by 3
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Citing papers per year: 2026: 1.
1 paper.