Edinburgh Research Explorer

Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationThe 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Place of PublicationNew Orleans, Louisiana
PublisherAssociation for Computational Linguistics
Pages708-713
Number of pages6
DOIs
Publication statusPublished - 6 Jun 2018
Event16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Hyatt Regency New Orleans Hotel, New Orleans, United States
Duration: 1 Jun 20186 Jun 2018
http://naacl2018.org/

Conference

Conference16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Abbreviated titleNAACL HLT 2018
CountryUnited States
CityNew Orleans
Period1/06/186/06/18
Internet address

Abstract

An essential aspect to understanding narratives is to grasp the interaction between characters in a story and the actions they take. We examine whether computational models can capture this interaction, when both character attributes and actions are expressed as complex natural language descriptions. We propose role-playing games as a testbed for this problem, and introduce a large corpus1 of game transcripts collected from online discussion forums. Using neural language models which combine character and action descriptions from these stories, we show that we can learn the latent ties. Action sequences are better predicted when the character performing the action is also taken into account, and vice versa for character attributes.
1: http://groups.inf.ed.ac.uk/cup/ddd/

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