@inproceedings{chen-etal-2022-iucl,
    title = "{IUCL} at {WASSA} 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection",
    author = {Chen, Yue  and
      Ju, Yingnan  and
      K{\"u}bler, Sandra},
    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/jlcl-multiple-ingestion/2022.wassa-1.21/",
    doi = "10.18653/v1/2022.wassa-1.21",
    pages = "228--232",
    abstract = "Our system, IUCL, participated in the WASSA 2022 Shared Task on Empathy Detection and Emotion Classification. Our main goal in building this system is to investigate how the use of demographic attributes influences performance. Our (official) results show that our text-only systems perform very competitively, ranking first in the empathy detection task, reaching an average Pearson correlation of 0.54, and second in the emotion classification task, reaching a Macro-F of 0.572. Our systems that use both text and demographic data are less competitive."
}