<ul data-eligibleForWebStory="false"><li>Efficient planning, resource management, and consistent operations often rely on converting textual process documents into formal Business Process Model and Notation (BPMN) models.</li><li>Existing approaches, whether rule-based or machine-learning-based, struggle with writing styles and identifying parallel structures in process descriptions.</li><li>A new automated pipeline leveraging machine learning and large language models is introduced for extracting BPMN models from text, along with a newly annotated dataset to enhance training by including parallel gateways.</li><li>The proposed approach shows promising results in terms of reconstruction accuracy, providing a foundation to speed up BPMN model creation for organizations.</li></ul>

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text

Discover more