@inproceedings{lahnala-etal-2022-caisa,
title = "{CAISA} at {WASSA} 2022: Adapter-Tuning for Empathy Prediction",
author = "Lahnala, Allison and
Welch, Charles and
Flek, Lucie",
editor = "Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Barriere, Valentin and
Tafreshi, Shabnam and
Alqahtani, Sawsan and
Sedoc, Jo{\~a}o and
Klinger, Roman and
Balahur, Alexandra",
booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wassa-1.31",
doi = "10.18653/v1/2022.wassa-1.31",
pages = "280--285",
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",
}
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<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</abstract>
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%0 Conference Proceedings
%T CAISA at WASSA 2022: Adapter-Tuning for Empathy Prediction
%A Lahnala, Allison
%A Welch, Charles
%A Flek, Lucie
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Tafreshi, Shabnam
%Y Alqahtani, Sawsan
%Y Sedoc, João
%Y Klinger, Roman
%Y Balahur, Alexandra
%S Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F lahnala-etal-2022-caisa
%X 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
%R 10.18653/v1/2022.wassa-1.31
%U https://aclanthology.org/2022.wassa-1.31
%U https://doi.org/10.18653/v1/2022.wassa-1.31
%P 280-285
Markdown (Informal)
[CAISA at WASSA 2022: Adapter-Tuning for Empathy Prediction](https://aclanthology.org/2022.wassa-1.31) (Lahnala et al., WASSA 2022)
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.