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An ion exchange mechanism inspired story ending generator for different characters

Xinyu Jiang, Qi Zhang, Chongyang Shi*, Kaiying Jiang, Liang Hu, Shoujin Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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.

Original languageEnglish
Title of host publicationMachine learning and knowledge discovery in databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II
EditorsMassih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages553-570
Number of pages18
ISBN (Electronic)9783031263903
ISBN (Print)9783031263897
DOIs
Publication statusPublished - 2023
EventThe European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2022 - Hybrid, Grenoble, France
Duration: 19 Sept 2022 → 23 Sept 2022

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume13714
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThe European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2022
Abbreviated titleECML–PKDD 2022
Country/TerritoryFrance
CityHybrid, Grenoble
Period19/09/22 → 23/09/22

Keywords

  • Story ending generation
  • Character-oriented
  • Neural network

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