@article{rim-pustejovsky-2026-missing,
    title = "Missing Links: {LLM}-Augmentation of Event Triggers of State Changes in the {O}pen{PI} Dataset",
    author = "Rim, Kyeongmin  and
      Pustejovsky, James",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.939/",
    pages = "11992--12006",
    abstract = "Effective computational understanding of procedural text requires modeling not just the state changes that occur (entity transformations), but also the specific actions that cause them (event triggers). A lack of datasets that explicitly link these two primary information sources has hindered progress in theory-oriented research and applications of NLP. This paper presents two primary contributions: (i) a new silver-standard dataset where event trigger annotations are added to existing state-change data on task-oriented procedural text, enabling both theoretical investigation and practical benchmarking; and (ii) inverse annotation, a framework for recovering missing linguistic annotations from existing semantic annotations{---}which we apply to recover event triggers from OpenPI{'}s state-change outcomes. We provide detailed pipeline analysis including error modes and quality filtering, and validate the dataset through comprehensive baseline evaluation of diverse trigger detection systems. Our work delivers both a reusable methodological framework applicable to other annotation recovery tasks and a new benchmark resource for modeling the relationship between linguistic actions and their semantic outcomes in procedural domains."
}