Abstract
We build a system that leverages adapters, a light weight and efficient method for leveraging large language models to perform the task Em- pathy and Distress prediction tasks for WASSA 2022. In our experiments, we find that stacking our empathy and distress adapters on a pre-trained emotion lassification adapter performs best compared to full fine-tuning approaches and emotion feature concatenation. We make our experimental code publicly available- Anthology ID:
- 2022.wassa-1.31
- Volume:
- Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Jeremy Barnes, Orphée De Clercq, Valentin Barriere, Shabnam Tafreshi, Sawsan Alqahtani, João Sedoc, Roman Klinger, Alexandra Balahur
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 280–285
- Language:
- URL:
- https://aclanthology.org/2022.wassa-1.31
- DOI:
- 10.18653/v1/2022.wassa-1.31
- Cite (ACL):
- Allison Lahnala, Charles Welch, and Lucie Flek. 2022. CAISA at WASSA 2022: Adapter-Tuning for Empathy Prediction. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 280–285, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- CAISA at WASSA 2022: Adapter-Tuning for Empathy Prediction (Lahnala et al., WASSA 2022)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2022.wassa-1.31.pdf
- Code
- caisa-lab/wassa-empathy-adapters
- Data
- CARER