Complementary Story Generation based on Latent Variable Hierarchical Recurrent Encoder-Decoder

Abstract

We study the problem of automatically generating a well-coherent story by a computer. In general, stories such as novels and movies are required to be coherent, that is, the beginning and ending of the story need to be properly connected by multiple related events with emotional ups and downs. Against this background, we have set up a new task to generate a story by giving the first and last sentences of the story as input and complementing them. In this paper, we propose a model that considers information of the last sentence in the process of generating sentences forward from the first sentence of the story. We evaluate the generated story using the story coherence evaluation model based on the general-purpose language model newly made for this paper, instead of the conventional evaluation metrics that compares the generated story with ground-truth. Through experiments, we show that the proposed method can generate a well-coherent story.