Infusing Commonsense World Models with Graph Knowledge
- URL: http://arxiv.org/abs/2301.05746v1
- Date: Fri, 13 Jan 2023 19:58:27 GMT
- Title: Infusing Commonsense World Models with Graph Knowledge
- Authors: Alexander Gurung, Mojtaba Komeili, Arthur Szlam, Jason Weston, and
Jack Urbanek
- Abstract summary: We study the setting of generating narratives in an open world text adventure game.
A graph representation of the underlying game state can be used to train models that consume and output both grounded graph representations and natural language descriptions and actions.
- Score: 89.27044249858332
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While language models have become more capable of producing compelling
language, we find there are still gaps in maintaining consistency, especially
when describing events in a dynamically changing world. We study the setting of
generating narratives in an open world text adventure game, where a graph
representation of the underlying game state can be used to train models that
consume and output both grounded graph representations and natural language
descriptions and actions. We build a large set of tasks by combining
crowdsourced and simulated gameplays with a novel dataset of complex actions in
order to to construct such models. We find it is possible to improve the
consistency of action narration models by training on graph contexts and
targets, even if graphs are not present at test time. This is shown both in
automatic metrics and human evaluations. We plan to release our code, the new
set of tasks, and best performing models.
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