@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://preview.aclanthology.org/ingest-emnlp/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"
}