A-PGRD: Attention-based automatic business process model generation from RPA process description

Abstract

Robotic process automation (RPA), a tool driven by business processes as the kernel, continues to heat up in the business community. However, process-centric RPA modeling lacks an effective means. To address this problem, we propose a method for automatic process acquisition using RPA process descriptions as input. Existing deep learning process generation methods cannot be applied at the phrase level and have low accuracy at the sentence level. The proposed neural network method is based on an attention mechanism for automatic business process model generation from RPA process descriptions (A-PGRD). The approach analyzes easily accessible and unstructured natural language text documents, constructs a non-autoregressive neural network with an attention mechanism to retrieve the business process hierarchy, and generates a tree-like business process graph using unsupervised automation. Through K-fold cross-validation, the method achieves an accuracy of 41.7% on the manually collected open-source RPA business process dataset. Compared with the previous method, the method improves the learning efficiency by 23%–27%. The obtained results can be applied to the RPA tool to better optimize the business process and thus help organizations gain an edge over their competition.