  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.
