  Understanding procedural text requires tracking entities, actions and effects
as the narrative unfolds. We focus on the challenging real-world problem of
action-graph extraction from material science papers, where language is highly
specialized and data annotation is expensive and scarce. We propose a novel
approach, Text2Quest, where procedural text is interpreted as instructions for
an interactive game. A learning agent completes the game by executing the
procedure correctly in a text-based simulated lab environment. The framework
can complement existing approaches and enables richer forms of learning
compared to static texts. We discuss potential limitations and advantages of
the approach, and release a prototype proof-of-concept, hoping to encourage
research in this direction.
