BePT: A Behavior-based Process Translator for Interpreting and Understanding Process Models
It addresses confusion in interpreting shared process models, an incremental improvement for domain-specific applications.
The paper tackles the problem of generating natural language descriptions for process models to aid user understanding, proposing BePT, which outperforms state-of-the-art baselines in experiments.
Sharing process models on the web has emerged as a common practice. Users can collect and share their experimental process models with others. However, some users always feel confused about the shared process models for lack of necessary guidelines or instructions. Therefore, several process translators have been proposed to explain the semantics of process models in natural language (NL). We find that previous studies suffer from information loss and generate semantically erroneous descriptions that diverge from original model behaviors. In this paper, we propose a novel process translator named BePT (Behavior-based Process Translator) based on the encoder-decoder paradigm, encoding a process model into a middle representation and decoding the representation into NL descriptions. Our theoretical analysis demonstrates that BePT satisfies behavior correctness, behavior completeness and description minimality. The qualitative and quantitative experiments show that BePT outperforms the state-of-the-art baselines.