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.- Anthology ID:
- 2024.wassa-1.35
- Volume:
- Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Orphée De Clercq, Valentin Barriere, Jeremy Barnes, Roman Klinger, João Sedoc, Shabnam Tafreshi
- Venues:
- WASSA | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 404–411
- Language:
- URL:
- https://aclanthology.org/2024.wassa-1.35
- DOI:
- Cite (ACL):
- Shaz Furniturewala and Kokil Jaidka. 2024. Empaths at WASSA 2024 Empathy and Personality Shared Task: Turn-Level Empathy Prediction Using Psychological Indicators. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 404–411, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Empaths at WASSA 2024 Empathy and Personality Shared Task: Turn-Level Empathy Prediction Using Psychological Indicators (Furniturewala & Jaidka, WASSA-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.wassa-1.35.pdf