@inproceedings{liyanage-etal-2023-augmenting,
    title = "Augmenting {R}eddit Posts to Determine Wellness Dimensions impacting Mental Health",
    author = "Liyanage, Chandreen  and
      Garg, Muskan  and
      Mago, Vijay  and
      Sohn, Sunghwan",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "Proceedings of the 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.bionlp-1.27/",
    doi = "10.18653/v1/2023.bionlp-1.27",
    pages = "306--312",
    abstract = "Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text. As the distribution of WD on social media data is intrinsically imbalanced, we experiment the generative AI techniques for data augmentation to enable further improvement in the pre-screening task of classifying WD. To this end, we propose a simple yet effective data augmentation approach through prompt-based Generative AI models, and evaluate the ROUGE scores and syntactic/ semantic similarity among existing interpretations and augmented data. Our approach with ChatGPT model surpasses all the other methods and achieves improvement over baselines such as Easy-Data Augmentation (EDA) and Backtranslation (BT)."
}