@inproceedings{shichman-etal-2024-propbank,
    title = "{P}rop{B}ank-Powered Data Creation: Utilizing Sense-Role Labelling to Generate Disaster Scenario Data",
    author = "Shichman, Mollie Frances  and
      Bonial, Claire  and
      Hudson, Taylor A.  and
      Blodgett, Austin  and
      Ferraro, Francis  and
      Rudinger, Rachel",
    editor = "Bonial, Claire  and
      Bonn, Julia  and
      Hwang, Jena D.",
    booktitle = "Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.dmr-1.1",
    pages = "1--10",
    abstract = "For human-robot dialogue in a search-and-rescue scenario, a strong knowledge of the conditions and objects a robot will face is essential for effective interpretation of natural language instructions. In order to utilize the power of large language models without overwhelming the limited storage capacity of a robot, we propose PropBank-Powered Data Creation. PropBank-Powered Data Creation is an expert-in-the-loop data generation pipeline which creates training data for disaster-specific language models. We leverage semantic role labeling and Rich Event Ontology resources to efficiently develop seed sentences for fine-tuning a smaller, targeted model that could operate onboard a robot for disaster relief. We developed 32 sentence templates, which we used to make 2 seed datasets of 175 instructions for earthquake search and rescue and train derailment response. We further leverage our seed datasets as evaluation data to test our baseline fine-tuned models.",
}