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Creators/Authors contains: "Chaturvedi, Snigdha"

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  1. ; ; (, The Association for Computational Linguistics)
    Free, publicly-accessible full text available July 27, 2026
  2. ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available April 29, 2026
  3. ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available January 1, 2026
  4. ; ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available November 12, 2025
  5. ; ; (, Association for Computational Linguistics)
    Full Text Available 
  6. ; ; ; ; ; ; (, Association for Computational Linguistics)
    Full Text Available 
  7. ; ; ; ; ; ; (, Association for Computational Linguistics)
    Accepted Manuscript Full Text Available
  8. ; ; (, Association for Computational Linguistics)
  9. ; ; ; ; (, Association for Computational Linguistics)
  10. ; ; ; ; ; ; (, Findings of the Association for Computational Linguistics: EMNLP 2023)
    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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