Different from classical neural generation models, our generation model is in a two-stage generation manner, motivated by coarse-to-fine decoding (Dong and Lapata, 2018).
given a fluent sentence as the input, in the first stage, a Planner selects the positions of where to insert reparandum; in the second stage, a Generator generates disfluent segments accordingly for the predicted areas.