@inproceedings{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",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.939/",
doi = "10.63317/4ga5mnybeeam",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Missing Links: LLM-Augmentation of Event Triggers of State Changes in the OpenPI Dataset
%A Rim, Kyeongmin
%A Pustejovsky, James
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F rim-pustejovsky-2026-missing
%X 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.
%R 10.63317/4ga5mnybeeam
%U https://aclanthology.org/2026.lrec-1.939/
%U https://doi.org/10.63317/4ga5mnybeeam
%P 11992-12006
Markdown (Informal)
[Missing Links: LLM-Augmentation of Event Triggers of State Changes in the OpenPI Dataset](https://aclanthology.org/2026.lrec-1.939/) (Rim & Pustejovsky, LREC 2026)
ACL