{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/event-representations-for-automated-story","title":"Event Representations for Automated Story Generation with Deep Neural Nets","arxiv_id":"1706.01331","date":"2017-06-05","proceeding":null,"authors":["Lara J. Martin","Prithviraj Ammanabrolu","Xinyu Wang","William Hancock","Shruti Singh","Brent Harrison","Mark O. Riedl"],"abstract":"Automated story generation is the problem of automatically selecting a\nsequence of events, actions, or words that can be told as a story. We seek to\ndevelop a system that can generate stories by learning everything it needs to\nknow from textual story corpora. To date, recurrent neural networks that learn\nlanguage models at character, word, or sentence levels have had little success\ngenerating coherent stories. We explore the question of event representations\nthat provide a mid-level of abstraction between words and sentences in order to\nretain the semantic information of the original data while minimizing event\nsparsity. We present a technique for preprocessing textual story data into\nevent sequences. We then present a technique for automated story generation\nwhereby we decompose the problem into the generation of successive events\n(event2event) and the generation of natural language sentences from events\n(event2sentence). We give empirical results comparing different event\nrepresentations and their effects on event successor generation and the\ntranslation of events to natural language.","url_abs":"http://arxiv.org/abs/1706.01331v3","url_pdf":"http://arxiv.org/pdf/1706.01331v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"event-representations-for-automated-story","repo_url":"https://github.com/lara-martin/ASTER","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"event-expansion","task_name":"Event Expansion"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"story-generation","task_name":"Story Generation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[{"slug":"movieplotevents","name":"MoviePlotEvents","full_name":"CMU Movie Summary Corpus with Events"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.01331","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}