  Automatic process discovery from textual process documentations is highly
desirable to reduce time and cost of Business Process Management (BPM)
implementation in organizations. However, existing automatic process discovery
approaches mainly focus on identifying activities out of the documentations.
Deriving the structural relationships between activities, which is important in
the whole process discovery scope, is still a challenge. In fact, a business
process has latent semantic hierarchical structure which defines different
levels of detail to reflect the complex business logic. Recent findings in
neural machine learning area show that the meaningful linguistic structure can
be induced by joint language modeling and structure learning. Inspired by these
findings, we propose to retrieve the latent hierarchical structure present in
the textual business process documents by building a neural network that
leverages a novel recurrent architecture, Ordered Neurons LSTM (ON-LSTM), with
process-level language model objective. We tested the proposed approach on data
set of Process Description Documents (PDD) from our practical Robotic Process
Automation (RPA) projects. Preliminary experiments showed promising results.
