An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters

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

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

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.

源语言英语
主期刊名Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings
编辑Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
出版商Springer Science and Business Media Deutschland GmbH
553-570
页数18
ISBN(印刷版)9783031263897
DOI
出版状态已出版 - 2023
活动22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022 - Grenoble, 法国
期限: 19 9月 202223 9月 2022

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13714 LNAI
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022
国家/地区法国
Grenoble
时期19/09/2223/09/22

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