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

arXiv: https://arxiv.org/abs/2507.08362 · HTML: https://mlsanity.com/paper/2507.08362/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Large Language Models can accomplish Business Process Management Tasks](https://mlsanity.com/paper/2307.09923/) (2023)
- [Process Modeling With Large Language Models](https://mlsanity.com/paper/2403.07541/) (2024)
- [PET: An Annotated Dataset for Process Extraction from Natural Language Text](https://mlsanity.com/paper/2203.04860/) (2022)
- [Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text](https://mlsanity.com/paper/2305.03960/) (2023)
- [A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models](https://mlsanity.com/paper/2407.18540/) (2024)
- [Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future Challenges](https://mlsanity.com/paper/2110.03754/) (2021)

## Built on by (most cited first, 1 of 1)

- [Automatic Generation of Executable BPMN Models from Medical Guidelines](https://mlsanity.com/paper/2604.07817/) (2026)

## Abstract (arXiv)

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.