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We follow the step-by-step approach to neural data-to-text generation we proposed in Moryossef et al. (2019) , in which the generation process is divided into a text-planning stage followed by a plan-realization stage. We suggest four extensions to that framework: (1) we introduce a trainable neural planning component that can generate effective plans several orders of magnitude faster than the original planner; (2) we incorporate typing hints that improve the model's ability to deal withdoi:10.18653/v1/w19-8645 dblp:conf/inlg/MoryossefGD19 fatcat:7gudhkre2vc4dj5hfe74zyvasa