Affective and Dynamic Beam Search for Story Generation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917628050669568 |
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| author | Huang, Tenghao Qasemi, Ehsan Li, Bangzheng Wang, He Brahman, Faeze Chen, Muhao Chaturvedi, Snigdha |
| author_facet | Huang, Tenghao Qasemi, Ehsan Li, Bangzheng Wang, He Brahman, Faeze Chen, Muhao Chaturvedi, Snigdha |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_15079 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Affective and Dynamic Beam Search for Story Generation Huang, Tenghao Qasemi, Ehsan Li, Bangzheng Wang, He Brahman, Faeze Chen, Muhao Chaturvedi, Snigdha Computation and Language Artificial Intelligence Machine Learning 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. |
| title | Affective and Dynamic Beam Search for Story Generation |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2310.15079 |