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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Baccianella, S., Esuli, A., Sebastani, F.: SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: The 7th Conference on International Language Resources and Evaluation (2010)
Bickmore, T., Schulman, D., Yin, L.: Engagement vs. Deceit: Virtual humans with human autobiographies. In: Ruttkay, Z., Kipp, M., Nijholt, A., Vilhjálmsson, H.H. (eds.) IVA 2009. LNCS, vol. 5773, pp. 6–19. Springer, Heidelberg (2009)
Haveliwala, T.H.: Topic-sensitive PageRank. In: The 11th International World Wide Web Conference (2002)
Li, B., Lee-Urban, S., Appling, D.S., Riedl, M.O.: Crowdsourcing narrative intelligence. Advances in Cognitive Systems 2 (2012)
Li, B., Lee-Urban, S., Johnston, G., Riedl, M.O.: Story generation with crowdsourced plot graphs. In: The 27th AAAI Conference on Artificial Intelligence (2013)
Mairesse, F., Walker, M.A.: PERSONAGE: Personality generation for dialogue. In: The 45th Annual Meeting of the Association for Computational Linguistics (2007)
McIntyre, N., Lapata, M.: Plot induction and evolutionary search for story generation. In: The 48th Annual Meeting of the Association for Computational Linguistics, pp. 1562–1572 (2010)
Michel, J.B., Shen, Y., Aiden, A., Veres, A., Gray, M., Brockman, W., Pickett, J., Hoiberg, D., Clancy, D., Norvig, P., Orwant, J., Pinker, S., Nowak, M., Aiden, E.: Quantitative analysis of culture using millions of digitized books. Science 331, 176–182 (2011)
Rishes, E., Lukin, S.M., Elson, D.K., Walker, M.A.: Generating different story tellings from semantic representations of narrative. In: Koenitz, H., Sezen, T.I., Ferri, G., Haahr, M., Sezen, D., C̨atak, G. (eds.) ICIDS 2013. LNCS, vol. 8230, pp. 192–204. Springer, Heidelberg (2013)
Rowe, J.P., Ha, E.Y., Lester, J.C.: Archetype-driven character dialogue generation for interactive narrative. In: Prendinger, H., Lester, J.C., Ishizuka, M. (eds.) IVA 2008. LNCS (LNAI), vol. 5208, pp. 45–58. Springer, Heidelberg (2008)
Swanson, R., Gordon, A.: Say anything: Using textual case-based reasoning to enable open-domain interactive storytelling. ACM Transactions on Interactive Intelligent Systems 2, 1–35 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-09767-1_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09766-4
Online ISBN: 978-3-319-09767-1
eBook Packages: Computer ScienceComputer Science (R0)