Controllable and Editable Neural Story Plot Generation via Control-and-Edit Transformer

Language-modeling-based methods for story plot generation aim to generate a plot with a language model (LM). LM methods have limitations of user-assist plot generation of goal control, refinement for editing, causing the generated plots not clear sense for specific goal, lack coherence, and edit flexible. We present a control-and-edit transformer technique which uses controlled imitation learning of editing distance from dynamic programming to support deleting policy, inserting policy, a weighting-reward with prepossess of corpus statistic, and measures continues reward for the controlled goal. Automated evaluation and Haman judgement show our method is promising in comparison with the baselines.