@inproceedings{huang-etal-2023-affective,
title = "Affective and Dynamic Beam Search for Story Generation",
author = "Huang, Tenghao and
Qasemi, Ehsan and
Li, Bangzheng and
Wang, He and
Brahman, Faeze and
Chen, Muhao and
Chaturvedi, Snigdha",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.789",
doi = "10.18653/v1/2023.findings-emnlp.789",
pages = "11792--11806",
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.",
}
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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.</abstract>
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%0 Conference Proceedings
%T Affective and Dynamic Beam Search for Story Generation
%A Huang, Tenghao
%A Qasemi, Ehsan
%A Li, Bangzheng
%A Wang, He
%A Brahman, Faeze
%A Chen, Muhao
%A Chaturvedi, Snigdha
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F huang-etal-2023-affective
%X 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.
%R 10.18653/v1/2023.findings-emnlp.789
%U https://aclanthology.org/2023.findings-emnlp.789
%U https://doi.org/10.18653/v1/2023.findings-emnlp.789
%P 11792-11806
Markdown (Informal)
[Affective and Dynamic Beam Search for Story Generation](https://aclanthology.org/2023.findings-emnlp.789) (Huang et al., Findings 2023)
ACL
- Tenghao Huang, Ehsan Qasemi, Bangzheng Li, He Wang, Faeze Brahman, Muhao Chen, and Snigdha Chaturvedi. 2023. Affective and Dynamic Beam Search for Story Generation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11792–11806, Singapore. Association for Computational Linguistics.