A Character-Centric Neural Model for Automated Story Generation


  • Danyang Liu Shanghai Jiao Tong University
  • Juntao Li Peking University
  • Meng-Hsuan Yu Peking University
  • Ziming Huang IBM Research-China
  • Gongshen Liu Shanghai Jiao Tong University
  • Dongyan Zhao Peking University
  • Rui Yan Peking University




Automated story generation is a challenging task which aims to automatically generate convincing stories composed of successive plots correlated with consistent characters. Most recent generation models are built upon advanced neural networks, e.g., variational autoencoder, generative adversarial network, convolutional sequence to sequence model. Although these models have achieved prompting results on learning linguistic patterns, very few methods consider the attributes and prior knowledge of the story genre, especially from the perspectives of explainability and consistency. To fill this gap, we propose a character-centric neural storytelling model, where a story is created encircling the given character, i.e., each part of a story is conditioned on a given character and corresponded context environment. In this way, we explicitly capture the character information and the relations between plots and characters to improve explainability and consistency. Experimental results on open dataset indicate that our model yields meaningful improvements over several strong baselines on both human and automatic evaluations.




How to Cite

Liu, D., Li, J., Yu, M.-H., Huang, Z., Liu, G., Zhao, D., & Yan, R. (2020). A Character-Centric Neural Model for Automated Story Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(02), 1725-1732. https://doi.org/10.1609/aaai.v34i02.5536



AAAI Technical Track: Game Playing and Interactive Entertainment