Abstract
The ability to craft, tell, and understand stories is important for virtual agents that wish to communicate with human users and simulate human capabilities. We provide an overview of an end-to-end storytelling system named Scheherazade, which learns domain-specific narrative knowledge from crowdsourced stories, generates stories and discourses, and presents stories in natural language with diverse personal styles and sentiments. Extending previous work, this paper addresses discourse planning and text generation. Discourse planning selectively omits events using typicality of events derived from graph structures. Text generation considers language features computed directly from large-scale data sets such as the Google N-Gram Corpus and Project Gutenberg books. Learning from these data sets instills virtual agents with linguistic and social behavioral knowledge.
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Li, B., Thakkar, M., Wang, Y., Riedl, M.O. (2014). From Data to Storytelling Agents. In: Bickmore, T., Marsella, S., Sidner, C. (eds) Intelligent Virtual Agents. IVA 2014. Lecture Notes in Computer Science(), vol 8637. Springer, Cham. https://doi.org/10.1007/978-3-319-09767-1_35
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DOI: https://doi.org/10.1007/978-3-319-09767-1_35
Publisher Name: Springer, Cham
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