T-CVAE: Transformer-Based Conditioned Variational Autoencoder for Story Completion
TL
TL;DR
This paper presents a novel conditional variational autoencoder based on Transformer for missing plot generation that generates better story plots than state-of-the-art models in terms of readability, diversity and coherence.
Abstract
Story completion is a very challenging task of generating the missing plot for an incomplete story, which requires not only understanding but also inference of the given contextual clues. In this paper, we present a novel conditional variational autoencoder based on Transformer for missing plot generation. Our model uses shared attention layers for encoder and decoder, which make the most of the contextual clues, and a latent variable for learning the distribution of coherent story plots. Through drawing samples from the learned distribution, diverse reasonable plots can be generated. Both automatic and manual evaluations show that our model generates better story plots than state-of-the-art models in terms of readability, diversity and coherence.