AB  - Story Ending Generation is a task of generating a coherent and sensible ending for a given story The key challenges of this task are i how to obtain a good understanding of context ii how to capture hidden information between lines and iii how to obtain causal progression However recent machine learning models can only partially address these challenges due to the lack of causal entailment and consistency The key novelty in our proposed approach is to capture the hidden story by generating transitional commonsense sentences between each adjacent context sentence which substantially enriches causal and consistent story flow Specifically we adopt a soft causal relation using people s everyday commonsense knowledge to mimic the cognitive understanding process of readers We then enrich the story with causal reasoning and utilize dependency parsing to capture long range text relations Finally we apply multi level Graph Convolutional Networks to deliver enriched contextual information across different layers Both automatic and human evaluation results show that our proposed model can significantly improve the quality of generated story endings 2023 The Authors
AU  - Park, E
AU  - Wong, RK
AU  - Chu, VW
DA  - 2023/09/28
DO  - 10.3233/FAIA230472
EP  - 1850
JO  - Frontiers in Artificial Intelligence and Applications
PB  - IOS Press
PY  - 2023/09/28
SP  - 1843
TI  - Story Ending Generation Using Commonsense Casual Reasoning and Graph Convolutional Networks
VL  - 372
Y1  - 2023/09/28
Y2  - 2026/08/02
ER  -