Creative Help: A Story Writing Assistant

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9445)


We present Creative Help, an application that helps writers by generating suggestions for the next sentence in a story as it being written. Users can modify or delete suggestions according to their own vision of the unfolding narrative. The application tracks users’ changes to suggestions in order to measure their perceived helpfulness to the story, with fewer edits indicating more helpful suggestions. We demonstrate how the edit distance between a suggestion and its resulting modification can be used to comparatively evaluate different models for generating suggestions. We describe a generation model that uses case-based reasoning to find relevant suggestions from a large corpus of stories. The application shows that this model generates suggestions that are more helpful than randomly selected suggestions at a level of marginal statistical significance. By giving users control over the generated content, Creative Help provides a new opportunity in open-domain interactive storytelling.


Open-domain interactive narrative Writing aids Natural language generation 



The projects or efforts depicted were or are sponsored by the U. S. Army. The content or information presented does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.


  1. 1.
    Chambers, N., Jurafsky, D.: Unsupervised learning of narrative event chains. In: 46th Annual Meeting of the Association of Computational Linguistics, pp. 789–797 (2008)Google Scholar
  2. 2.
    Crowther, W., Woods, D., Black, K.: Colossal cave adventure (1976)Google Scholar
  3. 3.
    Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Comput. Linguist. 28(3), 245–288 (2002)CrossRefGoogle Scholar
  4. 4.
    Gordon, A.S., Swanson, R.: Identifying Personal Stories in Millions of Weblog Entries. In: 3rd International Conference on Weblogs and Social Media, Data Challenge Workshop, pp. 16–23 (2009)Google Scholar
  5. 5.
    Hatcher, E., Gospodnetic, O., McCandless, M.: Lucene in action. Manning Publications, Shelter Island (2004)Google Scholar
  6. 6.
    Heeringa, W.J.: Measuring dialect pronunciation differences using Levenshtein distance. Ph.D. thesis, Rijksuniversiteit Groningen (2004)Google Scholar
  7. 7.
    Klein, S., Aeschlimann, J., Balsiger, D.: Automatic novel writing: a status report. Wisconsin University (1973)Google Scholar
  8. 8.
    Lebowitz, M.: Story-telling as planning and learning. Poetics 14(6), 483–502 (1985)CrossRefGoogle Scholar
  9. 9.
    Lee, H., Chang, A., Peirsman, Y., Chambers, N., Surdeanu, M., Jurafsky, D.: Deterministic coreference resolution based on entity-centric, precision-ranked rules. Comput. Linguist. 39(4), 885–916 (2013)CrossRefGoogle Scholar
  10. 10.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10, 707–710 (1966)MathSciNetGoogle Scholar
  11. 11.
    Li, B., Lee-Urban, S., Johnston, G., Riedl, M.: Story Generation with Crowdsourced Plot Graphs. In: 27th AAAI Conference on Artificial Intelligence (2013)Google Scholar
  12. 12.
    Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The stanford coreNLP natural language processing toolkit. In: 52nd Annual Meeting of the Association for Computational Linguistics, pp. 55–60 (2014)Google Scholar
  13. 13.
    Meehan, J.R.: TALE-SPIN, an interactive program that writes stories. In: 5th International Joint Conference on Artificial Intelligence, pp. 91–98 (1977)Google Scholar
  14. 14.
    Noreen, E.W.: Computer intensive methods for hypothesis testing: an introduction (1989)Google Scholar
  15. 15.
    Pérez, R.P., Sharples, M.: MEXICA: a computer model of a cognitive account of creative writing. J. Exp. Theor. Artif. Intell. 13(2), 119–139 (2001)CrossRefMATHGoogle Scholar
  16. 16.
    Rahimtoroghi, E., Corcoran, T., Swanson, R., Walker, M.A., Sagae, K., Gordon, A.S.: Minimal narrative annotation schemes and their applications. In: 7th Intelligent Narrative Technologies Workshop (2014)Google Scholar
  17. 17.
    Sagae, K., Gordon, A.S., Dehghani, M., Metke, M., Kim, J.S., Gimbel, S.I., Tipper, C., Kaplan, J., Immordino-Yang, M.H.: A data-driven approach for classification of subjectivity in personal narratives. In: 2013 Workshop on Computational Models of Narrative, pp. 198–213 (2013)Google Scholar
  18. 18.
    Swanson, R., Gordon, A.S.: A comparison of retrieval models for open domain story generation. In: AAAI Spring Symposium on Intelligent Narrative Technologies II (2009)Google Scholar
  19. 19.
    Swanson, R., Gordon, A.S.: Say anything: using textual case-based reasoning to enable open-domain interactive storytelling. ACM Trans. Interact. Intell. Syst. 2(3), 1–35 (2012)CrossRefGoogle Scholar
  20. 20.
    Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. In: 48th Annual Meeting of the Association for Computational Linguistics, pp. 384–394 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute for Creative TechnologiesUniversity of Southern CaliforniaLos AngelesUSA

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