@inproceedings{hasan-etal-2023-curtin,
    title = "Curtin {OCAI} at {WASSA} 2023 Empathy, Emotion and Personality Shared Task: Demographic-Aware Prediction Using Multiple Transformers",
    author = "Hasan, Md Rakibul  and
      Hossain, Md Zakir  and
      Gedeon, Tom  and
      Soon, Susannah  and
      Rahman, Shafin",
    editor = "Barnes, Jeremy  and
      De Clercq, Orph{\'e}e  and
      Klinger, Roman",
    booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2023.wassa-1.47/",
    doi = "10.18653/v1/2023.wassa-1.47",
    pages = "536--541",
    abstract = "The WASSA 2023 shared task on predicting empathy, emotion and other personality traits consists of essays, conversations and articles in textual form and participants' demographic information in numerical form. To address the tasks, our contributions include (1) converting numerical information into meaningful text information using appropriate templates, (2) summarising lengthy articles, and (3) augmenting training data by paraphrasing. To achieve these contributions, we leveraged two separate T5-based pre-trained transformers. We then fine-tuned pre-trained BERT, DistilBERT and ALBERT for predicting empathy and personality traits. We used the Optuna hyperparameter optimisation framework to fine-tune learning rates, batch sizes and weight initialisation. Our proposed system achieved its highest performance {--} a Pearson correlation coefficient of 0.750 {--} on the onversation-level empathy prediction task1 . The system implementation is publicly available at https: //github.com/hasan-rakibul/WASSA23-empathy-emotion."
}