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Computer Science > Computation and Language

arXiv:2310.15079 (cs)
[Submitted on 23 Oct 2023]

Title:Affective and Dynamic Beam Search for Story Generation

Authors:Tenghao Huang, Ehsan Qasemi, Bangzheng Li, He Wang, Faeze Brahman, Muhao Chen, Snigdha Chaturvedi
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Abstract: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.
Comments: Accepted at EMNLP-findings 2023
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2310.15079 [cs.CL]
  (or arXiv:2310.15079v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2310.15079

Submission history

From: Tenghao Huang [view email]
[v1] Mon, 23 Oct 2023 16:37:14 UTC (9,849 KB)
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