Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text
This work addresses the challenge of automating BPMN model generation from text for organizations, but it is incremental as it builds on existing datasets and methods.
The paper tackled the problem of converting textual process documents into BPMN models, which is time-intensive and costly, by introducing an automated pipeline that leverages machine learning and large language models, resulting in adequate reconstruction accuracy with a newly annotated dataset of 15 documents containing 32 parallel gateways.
Efficient planning, resource management, and consistent operations often rely on converting textual process documents into formal Business Process Model and Notation (BPMN) models. However, this conversion process remains time-intensive and costly. Existing approaches, whether rule-based or machine-learning-based, still struggle with writing styles and often fail to identify parallel structures in process descriptions. This paper introduces an automated pipeline for extracting BPMN models from text, leveraging the use of machine learning and large language models. A key contribution of this work is the introduction of a newly annotated dataset, which significantly enhances the training process. Specifically, we augment the PET dataset with 15 newly annotated documents containing 32 parallel gateways for model training, a critical feature often overlooked in existing datasets. This addition enables models to better capture parallel structures, a common but complex aspect of process descriptions. The proposed approach demonstrates adequate performance in terms of reconstruction accuracy, offering a promising foundation for organizations to accelerate BPMN model creation.