  Process model extraction (PME) is a recently emerged interdiscipline between
natural language processing (NLP) and business process management (BPM), which
aims to extract process models from textual descriptions. Previous process
extractors heavily depend on manual features and ignore the potential relations
between clues of different text granularities. In this paper, we formalize the
PME task into the multi-grained text classification problem, and propose a
hierarchical neural network to effectively model and extract multi-grained
information without manually-defined procedural features. Under this structure,
we accordingly propose the coarse-to-fine (grained) learning mechanism,
training multi-grained tasks in coarse-to-fine grained order to share the
high-level knowledge for the low-level tasks. To evaluate our approach, we
construct two multi-grained datasets from two different domains and conduct
extensive experiments from different dimensions. The experimental results
demonstrate that our approach outperforms the state-of-the-art methods with
statistical significance and further investigations demonstrate its
effectiveness.
