In order to use our model to generate diverse disfluent sentences, we experimented with different variants of PG and found PG-NC-AD-ID produced better performances.
we finally chose PG-NC-AD-ID and the heuristic Planner applying the position choosing heuristic described in Section 3.2.1, since the model-based Planner always chose certain most probable positions, and generated less diverse disfluent sentences, which did not work well as augmented data GPT2 was used to replace the LSTM decoder and trained on a partial pretraining dataset to alleviate the domain gap.