An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters

Abstract

Story ending generation aims at generating reasonable endings for a given story context. Most existing studies in this area focus on generating coherent or diversified story endings, while they ignore that different characters may lead to different endings for a given story. In this paper, we propose a Character-oriented Story Ending Generator (CoSEG) to customize an ending for each character in a story. Specifically, we first propose a character modeling module to learn the personalities of characters from their descriptive experiences extracted from the story context. Then, inspired by the ion exchange mechanism in chemical reactions, we design a novel vector breaking/forming module to learn the intrinsic interactions between each character and the corresponding context through an analogical information exchange procedure. Finally, we leverage the attention mechanism to learn effective character-specific interactions and feed each interaction into a decoder to generate character-orient endings. Extensive experimental results and case studies demonstrate that CoSEG achieves significant improvements in the quality of generated endings compared with state-of-the-art methods, and it effectively customizes the endings for different characters.

Cite

Text

Jiang et al. "An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26390-3_32

Markdown

[Jiang et al. "An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/jiang2022ecmlpkdd-ion/) doi:10.1007/978-3-031-26390-3_32

BibTeX

@inproceedings{jiang2022ecmlpkdd-ion,
  title     = {{An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters}},
  author    = {Jiang, Xinyu and Zhang, Qi and Shi, Chongyang and Jiang, Kaiying and Hu, Liang and Wang, Shoujin},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2022},
  pages     = {553-570},
  doi       = {10.1007/978-3-031-26390-3_32},
  url       = {https://mlanthology.org/ecmlpkdd/2022/jiang2022ecmlpkdd-ion/}
}