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

Annie Louis, Charles Sutton

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 corpus 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.
Anthology ID:
N18-2111
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
708–713
Language:
URL:
https://aclanthology.org/N18-2111
DOI:
10.18653/v1/N18-2111
Bibkey:
Cite (ACL):
Annie Louis and Charles Sutton. 2018. Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 708–713, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts (Louis & Sutton, NAACL 2018)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/N18-2111.pdf
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