@inproceedings{shimizu-etal-2024-generating,
    title = "Generating Distributable Surrogate Corpus for Medical Multi-label Classification",
    author = "Shimizu, Seiji  and
      Yada, Shuntaro  and
      Wakamiya, Shoko  and
      Aramaki, Eiji",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Thompson, Paul  and
      Ondov, Brian",
    booktitle = "Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.cl4health-1.19",
    pages = "153--162",
    abstract = "In medical and social media domains, annotated corpora are often hard to distribute due to copyrights and privacy issues. To overcome this situation, we propose a new method to generate a surrogate corpus for a downstream task by using a text generation model. We chose a medical multi-label classification task, MedWeb, in which patient-generated short messages express multiple symptoms. We first fine-tuned text generation models with different prompting designs on the original corpus to obtain synthetic versions of that corpus. To assess the viability of the generated corpora for the downstream task, we compared the performance of multi-label classification models trained either on the original or the surrogate corpora. The results and the error analysis showed the difficulty of generating surrogate corpus in multi-label settings, suggesting text generation under complex conditions is not trivial. On the other hand, our experiment demonstrates that the generated corpus with a sentinel-based prompting is comparatively viable in a single-label (multiclass) classification setting.",
}

In medical and social media domains, annotated corpora are often hard to distribute due to copyrights and privacy issues. To overcome this situation, we propose a new method to generate a surrogate corpus for a downstream task by using a text generation model. We chose a medical multi-label classification task, MedWeb, in which patient-generated short messages express multiple symptoms. We first fine-tuned text generation models with different prompting designs on the original corpus to obtain synthetic versions of that corpus. To assess the viability of the generated corpora for the downstream task, we compared the performance of multi-label classification models trained either on the original or the surrogate corpora. The results and the error analysis showed the difficulty of generating surrogate corpus in multi-label settings, suggesting text generation under complex conditions is not trivial. On the other hand, our experiment demonstrates that the generated corpus with a sentinel-based prompting is comparatively viable in a single-label (multiclass) classification setting.
Shimizu, Seiji Yada, Shuntaro Wakamiya, Shoko Aramaki, Eiji
Generating Distributable Surrogate Corpus for Medical Multi-label Classification