Affective and Dynamic Beam Search for Story Generation
Tenghao Huang Ehsan Qasemi Bangzheng Li He Wang Faeze Brahman Muhao Chen Snigdha Chaturvedi
Storytelling's captivating potential makes it a fascinating research area,
with implications for entertainment, education, therapy, and cognitive studies.
In this paper, we propose Affective Story Generator (AffGen) for generating
interesting narratives. AffGen introduces "intriguing twists" in narratives by
employing two novel techniques-Dynamic Beam Sizing and Affective Reranking.
Dynamic Beam Sizing encourages less predictable, more captivating word choices
using a contextual multi-arm bandit model. Affective Reranking prioritizes
sentence candidates based on affect intensity. Our empirical evaluations, both
automatic and human, demonstrate AffGen's superior performance over existing
baselines in generating affectively charged and interesting narratives. Our
ablation study and analysis provide insights into the strengths and weaknesses
of AffGen.