@inproceedings{lin-etal-2023-ncuee,
    title = "{NCUEE}-{NLP} at {WASSA} 2023 Shared Task 1: Empathy and Emotion Prediction Using Sentiment-Enhanced {R}o{BERT}a Transformers",
    author = "Lin, Tzu-Mi  and
      Chang, Jung-Ying  and
      Lee, Lung-Hao",
    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/fix-sig-urls/2023.wassa-1.49/",
    doi = "10.18653/v1/2023.wassa-1.49",
    pages = "548--552",
    abstract = "This paper describes our proposed system design for the WASSA 2023 shared task 1. We propose a unified architecture of ensemble neural networks to integrate the original RoBERTa transformer with two sentiment-enhanced RoBERTa-Twitter and EmoBERTa models. For Track 1 at the speech-turn level, our best submission achieved an average Pearson correlation score of 0.7236, ranking fourth for empathy, emotion polarity and emotion intensity prediction. For Track 2 at the essay-level, our best submission obtained an average Pearson correlation score of 0.4178 for predicting empathy and distress scores, ranked first among all nine submissions."
}