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While language models have become more capable of producing compellinglanguage, we find there are still gaps in maintaining consistency, especiallywhen describing events in a dynamically changing world. We study the setting ofgenerating narratives in an open world text adventure game, where a graphrepresentation of the underlying game state can be used to train models thatconsume and output both grounded graph representations and natural languagedescriptions and actions. We build a large set of tasks by combiningcrowdsourced and simulated gameplays with a novel dataset of complex actions inorder to to construct such models. We find it is possible to improve theconsistency of action narration models by training on graph contexts andtargets, even if graphs are not present at test time. This is shown both inautomatic metrics and human evaluations. We plan to release our code, the newset of tasks, and best performing models.

Infusing Commonsense World Models with Graph Knowledge